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/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Statistics, University of Washington, 98105, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Department of Statistics, University of Washington, 98105, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gao", 
        "givenName": "Peter A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Applied Mathematics and Statistics, Colorado School of Mines, 80401, Golden, CO, USA", 
          "id": "http://www.grid.ac/institutes/grid.254549.b", 
          "name": [
            "Department of Applied Mathematics and Statistics, Colorado School of Mines, 80401, Golden, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Director", 
        "givenName": "Hannah M.", 
        "id": "sg:person.010773602033.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010773602033.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Atmospheric Sciences, University of Washington, 98105, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Department of Atmospheric Sciences, University of Washington, 98105, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bitz", 
        "givenName": "Cecilia M.", 
        "id": "sg:person.01200354123.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01200354123.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Departments of Statistics and Sociology, University of Washington, 98105, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Departments of Statistics and Sociology, University of Washington, 98105, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Raftery", 
        "givenName": "Adrian E.", 
        "id": "sg:person.01021773026.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021773026.47"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s40641-018-0113-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107245302", 
          "https://doi.org/10.1007/s40641-018-0113-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-016-2985-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051520871", 
          "https://doi.org/10.1007/s00382-016-2985-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13253-018-00348-w", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110639041", 
          "https://doi.org/10.1007/s13253-018-00348-w"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13253-020-00401-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1128718625", 
          "https://doi.org/10.1007/s13253-020-00401-7"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-11-09", 
    "datePublishedReg": "2021-11-09", 
    "description": "In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s13253-021-00480-0", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1134206", 
        "issn": [
          "1085-7117", 
          "1537-2693"
        ], 
        "name": "Journal of Agricultural, Biological and Environmental Statistics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "keywords": [
      "Arctic sea ice thickness", 
      "sea ice thickness", 
      "ice-covered regions", 
      "ice thickness", 
      "probabilistic forecasts", 
      "sea ice volume", 
      "Arctic Ocean", 
      "ice volume", 
      "sea ice", 
      "better forecasts", 
      "forecasts", 
      "improved calibration", 
      "Gaussian random fields", 
      "deterministic dynamic model", 
      "resource management", 
      "large errors", 
      "complete estimate", 
      "recent decades", 
      "two-stage model", 
      "random fields", 
      "statistical model", 
      "Ocean", 
      "Arctic", 
      "region", 
      "ice", 
      "calibration", 
      "sharp reduction", 
      "point prediction", 
      "thickness", 
      "prediction intervals", 
      "dynamic model", 
      "comparable accuracy", 
      "ensemble", 
      "supplementary material", 
      "estimates", 
      "aggregate quantity", 
      "model", 
      "poor calibration", 
      "shipping", 
      "temperature", 
      "volume", 
      "interval", 
      "changes", 
      "contour model", 
      "decades", 
      "prediction", 
      "quantity", 
      "error", 
      "field", 
      "future", 
      "accuracy", 
      "conditional", 
      "approach", 
      "materials", 
      "reduction", 
      "management", 
      "lines", 
      "months", 
      "method", 
      "paper", 
      "decisions"
    ], 
    "name": "Probabilistic Forecasts of Arctic Sea Ice Thickness", 
    "pagination": "280-302", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1142453069"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13253-021-00480-0"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13253-021-00480-0", 
      "https://app.dimensions.ai/details/publication/pub.1142453069"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:39", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_903.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s13253-021-00480-0"
  }
]

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

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

162 TRIPLES      22 PREDICATES      90 URIs      78 LITERALS      6 BLANK NODES