Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-07

AUTHORS

Yawen Guan, Christian Sampson, J. Derek Tucker, Won Chang, Anirban Mondal, Murali Haran, Deborah Sulsky

ABSTRACT

Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online. More... »

PAGES

1-20

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13253-019-00353-7

DOI

http://dx.doi.org/10.1007/s13253-019-00353-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111983049


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0406", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Geography and Environmental Geoscience", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Statistical and Applied Mathematical Sciences Institute", 
          "id": "https://www.grid.ac/institutes/grid.438085.2", 
          "name": [
            "North Carolina State University, Raleigh, USA", 
            "The Statistical and Applied Mathematical Sciences Institute, Durham, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Guan", 
        "givenName": "Yawen", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of North Carolina at Chapel Hill", 
          "id": "https://www.grid.ac/institutes/grid.10698.36", 
          "name": [
            "The Statistical and Applied Mathematical Sciences Institute, Durham, USA", 
            "The University of North Carolina at Chapel Hill, Chapel Hill, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sampson", 
        "givenName": "Christian", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sandia National Laboratories", 
          "id": "https://www.grid.ac/institutes/grid.474520.0", 
          "name": [
            "Sandia National Laboratories, Albuquerque, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tucker", 
        "givenName": "J. Derek", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Cincinnati", 
          "id": "https://www.grid.ac/institutes/grid.24827.3b", 
          "name": [
            "University of Cincinnati, Cincinnati, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chang", 
        "givenName": "Won", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Case Western Reserve University", 
          "id": "https://www.grid.ac/institutes/grid.67105.35", 
          "name": [
            "Case Western Reserve University, Cleveland, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mondal", 
        "givenName": "Anirban", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennsylvania State University", 
          "id": "https://www.grid.ac/institutes/grid.29857.31", 
          "name": [
            "Pennsylvania State University, University Park, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Haran", 
        "givenName": "Murali", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of New Mexico", 
          "id": "https://www.grid.ac/institutes/grid.266832.b", 
          "name": [
            "University of New Mexico, Albuquerque, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sulsky", 
        "givenName": "Deborah", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1023/b:visi.0000043755.93987.aa", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001093472", 
          "https://doi.org/10.1023/b:visi.0000043755.93987.aa"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-16541-2_7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005174674", 
          "https://doi.org/10.1007/978-3-642-16541-2_7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2008.10.040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006623734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011849757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/98jc01259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013288630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physd.2011.07.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019181840"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2012.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020166200"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/96jc02775", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021909006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1027059518", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4939-4020-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027059518", 
          "https://doi.org/10.1007/978-1-4939-4020-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4471-2458-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027180633", 
          "https://doi.org/10.1007/978-1-4471-2458-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4471-2458-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027180633", 
          "https://doi.org/10.1007/978-1-4471-2458-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2003jc002108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029281474"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/09-ba404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037786486"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9868.00294", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042110240"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(2002)083<0407:dihrpm>2.3.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043302866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.coldregions.2009.10.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044138375"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2005jc003334", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045661444"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2005jc003393", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049600497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10851-008-0129-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052507770", 
          "https://doi.org/10.1007/s10851-008-0129-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-015-9735-7_26", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052856851", 
          "https://doi.org/10.1007/978-94-015-9735-7_26"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/89.4.769", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059421210"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/asp028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059421744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/asp028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059421744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/130935367", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062871219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s1064827503426693", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062884102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/tech.2009.08040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064199703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/009053607000000163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064389044"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/15-sts524", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064395482"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1268522", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069420824"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5705/ss.202016.0217", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084473166"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-02-07", 
    "datePublishedReg": "2019-02-07", 
    "description": "Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s13253-019-00353-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4311115", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6932935", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.3004528", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.3660693", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1134206", 
        "issn": [
          "1085-7117", 
          "1537-2693"
        ], 
        "name": "Journal of Agricultural, Biological and Environmental Statistics", 
        "type": "Periodical"
      }
    ], 
    "name": "Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation", 
    "pagination": "1-20", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "89040366fd3cc5c07082970007bac4f2673e4987ac0eaf992bfd16c3d7d00c78"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13253-019-00353-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111983049"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13253-019-00353-7", 
      "https://app.dimensions.ai/details/publication/pub.1111983049"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000332_0000000332/records_121956_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs13253-019-00353-7"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s13253-019-00353-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13253-019-00353-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13253-019-00353-7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13253-019-00353-7'


 

This table displays all metadata directly associated to this object as RDF triples.

210 TRIPLES      21 PREDICATES      53 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13253-019-00353-7 schema:about anzsrc-for:04
2 anzsrc-for:0406
3 schema:author Nb75b1f63c64c4b11baf40ee8e3e40d9d
4 schema:citation sg:pub.10.1007/978-1-4471-2458-0
5 sg:pub.10.1007/978-1-4939-4020-2
6 sg:pub.10.1007/978-3-642-16541-2_7
7 sg:pub.10.1007/978-94-015-9735-7_26
8 sg:pub.10.1007/s10851-008-0129-7
9 sg:pub.10.1023/b:visi.0000043755.93987.aa
10 https://app.dimensions.ai/details/publication/pub.1027059518
11 https://doi.org/10.1016/j.coldregions.2009.10.003
12 https://doi.org/10.1016/j.csda.2012.12.001
13 https://doi.org/10.1016/j.neuroimage.2008.10.040
14 https://doi.org/10.1016/j.physd.2011.07.005
15 https://doi.org/10.1029/2003jc002108
16 https://doi.org/10.1029/2005jc003334
17 https://doi.org/10.1029/2005jc003393
18 https://doi.org/10.1029/96jc02775
19 https://doi.org/10.1029/98jc01259
20 https://doi.org/10.1093/biomet/89.4.769
21 https://doi.org/10.1093/biomet/asp028
22 https://doi.org/10.1111/1467-9868.00294
23 https://doi.org/10.1137/130935367
24 https://doi.org/10.1137/s1064827503426693
25 https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2
26 https://doi.org/10.1175/1520-0477(2002)083<0407:dihrpm>2.3.co;2
27 https://doi.org/10.1198/tech.2009.08040
28 https://doi.org/10.1214/009053607000000163
29 https://doi.org/10.1214/09-ba404
30 https://doi.org/10.1214/15-sts524
31 https://doi.org/10.2307/1268522
32 https://doi.org/10.5705/ss.202016.0217
33 schema:datePublished 2019-02-07
34 schema:datePublishedReg 2019-02-07
35 schema:description Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf sg:journal.1134206
40 schema:name Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation
41 schema:pagination 1-20
42 schema:productId N1121baa763b24039952bca5d2f983861
43 N9efb2dccb78e49b7b8a59bb8dac1877c
44 Nc0c807e6fba74a569b6e6a48ffcae1b0
45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111983049
46 https://doi.org/10.1007/s13253-019-00353-7
47 schema:sdDatePublished 2019-04-11T09:03
48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
49 schema:sdPublisher N66a12e862ee544c893d19009bf090850
50 schema:url https://link.springer.com/10.1007%2Fs13253-019-00353-7
51 sgo:license sg:explorer/license/
52 sgo:sdDataset articles
53 rdf:type schema:ScholarlyArticle
54 N06fc31d6db0c45b58638582322b74b38 schema:affiliation https://www.grid.ac/institutes/grid.266832.b
55 schema:familyName Sulsky
56 schema:givenName Deborah
57 rdf:type schema:Person
58 N1121baa763b24039952bca5d2f983861 schema:name dimensions_id
59 schema:value pub.1111983049
60 rdf:type schema:PropertyValue
61 N1c6e23a9df8e4e909b0d1cd9505e0abd rdf:first Nd1bec6f6a77746f79bb9d9781f6ba6bb
62 rdf:rest Nf66c583db74a4dba8250eda671d971e0
63 N3fe7d3bc74d74bfd8e98b1c3df25746b rdf:first Nd1e9a559eddf41f186e8aaf28380e498
64 rdf:rest N5555f3dc3bb041c7a9a9aabc147e1a8f
65 N5555f3dc3bb041c7a9a9aabc147e1a8f rdf:first N55b69763397e41aba68f286da06c91c3
66 rdf:rest Nc949bea127c444a981a05b85c4116247
67 N55b69763397e41aba68f286da06c91c3 schema:affiliation https://www.grid.ac/institutes/grid.474520.0
68 schema:familyName Tucker
69 schema:givenName J. Derek
70 rdf:type schema:Person
71 N66a12e862ee544c893d19009bf090850 schema:name Springer Nature - SN SciGraph project
72 rdf:type schema:Organization
73 N9efb2dccb78e49b7b8a59bb8dac1877c schema:name readcube_id
74 schema:value 89040366fd3cc5c07082970007bac4f2673e4987ac0eaf992bfd16c3d7d00c78
75 rdf:type schema:PropertyValue
76 Nb60521c611eb48928400f5e20fc8affb rdf:first Ne2da5f39dc3c46368d85a88bb10ff3c8
77 rdf:rest N1c6e23a9df8e4e909b0d1cd9505e0abd
78 Nb75b1f63c64c4b11baf40ee8e3e40d9d rdf:first Nd8e6579c27b84a7e8666a6d17412826b
79 rdf:rest N3fe7d3bc74d74bfd8e98b1c3df25746b
80 Nc0c807e6fba74a569b6e6a48ffcae1b0 schema:name doi
81 schema:value 10.1007/s13253-019-00353-7
82 rdf:type schema:PropertyValue
83 Nc949bea127c444a981a05b85c4116247 rdf:first Ndc0289feb6e24ce99894296d4992643d
84 rdf:rest Nb60521c611eb48928400f5e20fc8affb
85 Nd1bec6f6a77746f79bb9d9781f6ba6bb schema:affiliation https://www.grid.ac/institutes/grid.29857.31
86 schema:familyName Haran
87 schema:givenName Murali
88 rdf:type schema:Person
89 Nd1e9a559eddf41f186e8aaf28380e498 schema:affiliation https://www.grid.ac/institutes/grid.10698.36
90 schema:familyName Sampson
91 schema:givenName Christian
92 rdf:type schema:Person
93 Nd8e6579c27b84a7e8666a6d17412826b schema:affiliation https://www.grid.ac/institutes/grid.438085.2
94 schema:familyName Guan
95 schema:givenName Yawen
96 rdf:type schema:Person
97 Ndc0289feb6e24ce99894296d4992643d schema:affiliation https://www.grid.ac/institutes/grid.24827.3b
98 schema:familyName Chang
99 schema:givenName Won
100 rdf:type schema:Person
101 Ne2da5f39dc3c46368d85a88bb10ff3c8 schema:affiliation https://www.grid.ac/institutes/grid.67105.35
102 schema:familyName Mondal
103 schema:givenName Anirban
104 rdf:type schema:Person
105 Nf66c583db74a4dba8250eda671d971e0 rdf:first N06fc31d6db0c45b58638582322b74b38
106 rdf:rest rdf:nil
107 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
108 schema:name Earth Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
111 schema:name Physical Geography and Environmental Geoscience
112 rdf:type schema:DefinedTerm
113 sg:grant.3004528 http://pending.schema.org/fundedItem sg:pub.10.1007/s13253-019-00353-7
114 rdf:type schema:MonetaryGrant
115 sg:grant.3660693 http://pending.schema.org/fundedItem sg:pub.10.1007/s13253-019-00353-7
116 rdf:type schema:MonetaryGrant
117 sg:grant.4311115 http://pending.schema.org/fundedItem sg:pub.10.1007/s13253-019-00353-7
118 rdf:type schema:MonetaryGrant
119 sg:grant.6932935 http://pending.schema.org/fundedItem sg:pub.10.1007/s13253-019-00353-7
120 rdf:type schema:MonetaryGrant
121 sg:journal.1134206 schema:issn 1085-7117
122 1537-2693
123 schema:name Journal of Agricultural, Biological and Environmental Statistics
124 rdf:type schema:Periodical
125 sg:pub.10.1007/978-1-4471-2458-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027180633
126 https://doi.org/10.1007/978-1-4471-2458-0
127 rdf:type schema:CreativeWork
128 sg:pub.10.1007/978-1-4939-4020-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027059518
129 https://doi.org/10.1007/978-1-4939-4020-2
130 rdf:type schema:CreativeWork
131 sg:pub.10.1007/978-3-642-16541-2_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005174674
132 https://doi.org/10.1007/978-3-642-16541-2_7
133 rdf:type schema:CreativeWork
134 sg:pub.10.1007/978-94-015-9735-7_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052856851
135 https://doi.org/10.1007/978-94-015-9735-7_26
136 rdf:type schema:CreativeWork
137 sg:pub.10.1007/s10851-008-0129-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052507770
138 https://doi.org/10.1007/s10851-008-0129-7
139 rdf:type schema:CreativeWork
140 sg:pub.10.1023/b:visi.0000043755.93987.aa schema:sameAs https://app.dimensions.ai/details/publication/pub.1001093472
141 https://doi.org/10.1023/b:visi.0000043755.93987.aa
142 rdf:type schema:CreativeWork
143 https://app.dimensions.ai/details/publication/pub.1027059518 schema:CreativeWork
144 https://doi.org/10.1016/j.coldregions.2009.10.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044138375
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/j.csda.2012.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020166200
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.neuroimage.2008.10.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006623734
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.physd.2011.07.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019181840
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1029/2003jc002108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029281474
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1029/2005jc003334 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045661444
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1029/2005jc003393 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049600497
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1029/96jc02775 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021909006
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1029/98jc01259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013288630
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1093/biomet/89.4.769 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059421210
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1093/biomet/asp028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059421744
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1111/1467-9868.00294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042110240
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1137/130935367 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062871219
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1137/s1064827503426693 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062884102
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011849757
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1175/1520-0477(2002)083<0407:dihrpm>2.3.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043302866
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1198/tech.2009.08040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064199703
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1214/009053607000000163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064389044
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1214/09-ba404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037786486
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1214/15-sts524 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064395482
183 rdf:type schema:CreativeWork
184 https://doi.org/10.2307/1268522 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069420824
185 rdf:type schema:CreativeWork
186 https://doi.org/10.5705/ss.202016.0217 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084473166
187 rdf:type schema:CreativeWork
188 https://www.grid.ac/institutes/grid.10698.36 schema:alternateName University of North Carolina at Chapel Hill
189 schema:name The Statistical and Applied Mathematical Sciences Institute, Durham, USA
190 The University of North Carolina at Chapel Hill, Chapel Hill, USA
191 rdf:type schema:Organization
192 https://www.grid.ac/institutes/grid.24827.3b schema:alternateName University of Cincinnati
193 schema:name University of Cincinnati, Cincinnati, USA
194 rdf:type schema:Organization
195 https://www.grid.ac/institutes/grid.266832.b schema:alternateName University of New Mexico
196 schema:name University of New Mexico, Albuquerque, USA
197 rdf:type schema:Organization
198 https://www.grid.ac/institutes/grid.29857.31 schema:alternateName Pennsylvania State University
199 schema:name Pennsylvania State University, University Park, USA
200 rdf:type schema:Organization
201 https://www.grid.ac/institutes/grid.438085.2 schema:alternateName Statistical and Applied Mathematical Sciences Institute
202 schema:name North Carolina State University, Raleigh, USA
203 The Statistical and Applied Mathematical Sciences Institute, Durham, USA
204 rdf:type schema:Organization
205 https://www.grid.ac/institutes/grid.474520.0 schema:alternateName Sandia National Laboratories
206 schema:name Sandia National Laboratories, Albuquerque, USA
207 rdf:type schema:Organization
208 https://www.grid.ac/institutes/grid.67105.35 schema:alternateName Case Western Reserve University
209 schema:name Case Western Reserve University, Cleveland, USA
210 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...