{"id":"W2046745272","doi":"10.1002/cjs.10063","title":"Using temporal variability to improve spatial mapping with application to satellite data","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; National Aeronautics and Space Administration; National Science Foundation","keywords":"Computer science; Missing data; Satellite; Remote sensing; Grid; Filter (signal processing); Footprint; Statistical model; Kalman filter; Temporal resolution; Scalability; Component (thermodynamics); Data mining; Geography; Artificial intelligence; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002548705,0.00042704,0.000357092,0.00159159,0.0004286532,0.001356674,0.0005589446,0.0004342205,0.0009016658],"category_scores_gemma":[0.01008603,0.0002988122,0.0008282603,0.002159713,0.0003350584,0.001226768,0.001201957,0.0005482328,0.000173762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000678658,"about_ca_system_score_gemma":0.0009430848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02274164,"about_ca_topic_score_gemma":0.02068649,"domain_scores_codex":[0.9993819,0.0002762146,0.0000454467,0.0001601953,0.00009601496,0.00004021216],"domain_scores_gemma":[0.9967301,0.002302266,0.0002751514,0.0003406705,0.0003108785,0.00004094302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006931055,0.00005625887,0.04792837,0.00006722628,0.0002528234,0.00009192373,0.0001957715,0.7423822,0.002381434,0.01000168,0.001125014,0.1954481],"study_design_scores_gemma":[0.000003996381,0.00001048327,0.005157089,0.000009398311,0.00001447596,0.00001317195,0.00003243244,0.9896826,0.0004911966,0.003694698,0.0008814863,0.000009011637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1719279,0.0004038838,0.8230532,0.0004658837,0.00006459404,0.00004405124,0.000731218,0.001167556,0.002141671],"genre_scores_gemma":[0.7764783,0.0002747124,0.2211372,0.00004998363,0.00005943412,0.00006665428,0.0008639009,0.0001972864,0.0008725563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02274164,"threshold_uncertainty_score":0.04521853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03034373005954589,"score_gpt":0.2439479229199173,"score_spread":0.2136041928603714,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}