Item talk:Q326523

{

 "DOI": {
   "doi": "10.5066/p9glb4vb",
   "identifiers": [],
   "creators": [
     {
       "name": "Pastorino Gonzalez, Javier (Contractor) A",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": []
     },
     {
       "name": "Director, Joseph (Contractor) W",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": []
     },
     {
       "name": "A K Biswas",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": []
     },
     {
       "name": "Hawbaker, Todd J",
       "nameType": "Personal",
       "givenName": "Todd J",
       "familyName": "Hawbaker",
       "affiliation": [],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0003-0930-9154",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     }
   ],
   "titles": [
     {
       "title": "Burn probability predictions for the state of California, USA using an optimal set of spatio-temporal features."
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2022,
   "subjects": [
     {
       "subject": "Climatology, Ecology, Land Use Change"
     }
   ],
   "contributors": [],
   "dates": [
     {
       "date": "2015/2019",
       "dateType": "Available"
     },
     {
       "date": "2022",
       "dateType": "Issued"
     }
   ],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
     "citeproc": "dataset",
     "schemaOrg": "Dataset",
     "resourceType": "Dataset",
     "resourceTypeGeneral": "Dataset"
   },
   "relatedIdentifiers": [
     {
       "relationType": "IsCitedBy",
       "relatedIdentifier": "10.1145/3476883.3520228",
       "relatedIdentifierType": "DOI"
     }
   ],
   "relatedItems": [],
   "sizes": [],
   "formats": [],
   "version": null,
   "rightsList": [],
   "descriptions": [
     {
       "description": "Burn probability (BP) models the likelihood that a location could burn. However, predicting BP is extremely challenging, because fire behavior varies strongly among landscapes and with changing weather conditions and wildfire spread simulations are computationally intensive and require integration of data with large spatial and temporal variability. In this data release we include the monthly BP estimation for the state of California, USA for the 2015-2019 period produced using a machine learning model and two different sets of input features. For the first case, the baseline, the model used all available input features to predict BP. The second output set corresponds to the BP predictions when the model used only the set of optimal features as determined in the cited paper.",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://www.sciencebase.gov/catalog/item/621e7e17d34ee0c6b389a977",
   "contentUrl": null,
   "metadataVersion": 0,
   "schemaVersion": "http://datacite.org/schema/kernel-4",
   "source": "mds",
   "isActive": true,
   "state": "findable",
   "reason": null,
   "viewCount": 0,
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   "referenceCount": 0,
   "citationCount": 1,
   "partCount": 0,
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   "created": "2022-04-15T17:57:47Z",
   "registered": "2022-04-15T17:57:48Z",
   "published": null,
   "updated": "2022-04-15T17:57:48Z"
 }

}