Ontology type: schema:Chapter
1984
AUTHORSJeff Sullivan
ABSTRACTThe probability kriging technique is an improvement on the distribution free indicator kriging technique for obtaining conditional recoverable reserves. Probability kriging is similar to indicator kriging in that both techniques utilize indicator data and no assumption concerning the shape of the conditional distribution is made. Indicator kriging however does not utilize some easily obtainable information which causes, in certain cases, the indicator kriging estimator to be smoothed, conditionally biased, and in general a poor local estimator. The cases where indicator kriging performs poorly will be identified and it will be shown that by including additional information, through the probability kriging estimator, that the quality of the estimator will be improved. The probability kriging technique is then tested on a gold deposit and the results are presented. More... »
PAGES365-384
Geostatistics for Natural Resources Characterization
ISBN
978-94-010-8157-3
978-94-009-3699-7
http://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22
DOIhttp://dx.doi.org/10.1007/978-94-009-3699-7_22
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1010762596
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/2008/0806",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information Systems",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Stanford University",
"id": "https://www.grid.ac/institutes/grid.168010.e",
"name": [
"Department of Applied Earth Sciences, Stanford University School of Earth Sciences, Stanford, California, USA"
],
"type": "Organization"
},
"familyName": "Sullivan",
"givenName": "Jeff",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-94-009-3699-7_19",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050704907",
"https://doi.org/10.1007/978-94-009-3699-7_19"
],
"type": "CreativeWork"
}
],
"datePublished": "1984",
"datePublishedReg": "1984-01-01",
"description": "The probability kriging technique is an improvement on the distribution free indicator kriging technique for obtaining conditional recoverable reserves. Probability kriging is similar to indicator kriging in that both techniques utilize indicator data and no assumption concerning the shape of the conditional distribution is made. Indicator kriging however does not utilize some easily obtainable information which causes, in certain cases, the indicator kriging estimator to be smoothed, conditionally biased, and in general a poor local estimator. The cases where indicator kriging performs poorly will be identified and it will be shown that by including additional information, through the probability kriging estimator, that the quality of the estimator will be improved. The probability kriging technique is then tested on a gold deposit and the results are presented.",
"editor": [
{
"familyName": "Verly",
"givenName": "Georges",
"type": "Person"
},
{
"familyName": "David",
"givenName": "Michel",
"type": "Person"
},
{
"familyName": "Journel",
"givenName": "Andre G.",
"type": "Person"
},
{
"familyName": "Marechal",
"givenName": "Alain",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-94-009-3699-7_22",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-94-010-8157-3",
"978-94-009-3699-7"
],
"name": "Geostatistics for Natural Resources Characterization",
"type": "Book"
},
"name": "Conditional Recovery Estimation Through Probability Kriging \u2014 Theory and Practice",
"pagination": "365-384",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-94-009-3699-7_22"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"3345e61a0539ad6c9ba9683bd3b75de3c2f23241d1ad9320458b416b49aea489"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1010762596"
]
}
],
"publisher": {
"location": "Dordrecht",
"name": "Springer Netherlands",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-94-009-3699-7_22",
"https://app.dimensions.ai/details/publication/pub.1010762596"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-15T16:58",
"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/v90/0000000001_0000000264/records_8678_00000018.jsonl",
"type": "Chapter",
"url": "http://link.springer.com/10.1007/978-94-009-3699-7_22"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1007/978-94-009-3699-7_22'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'
This table displays all metadata directly associated to this object as RDF triples.
83 TRIPLES
23 PREDICATES
28 URIs
20 LITERALS
8 BLANK NODES