{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006515354,0.000110185,0.0001503063,0.00008652663,0.000141628,0.00008316023,0.0004356896,0.00004092217,0.0002072816],"category_scores_gemma":[0.0004515618,0.0001024951,0.000009918734,0.000218156,0.0000945229,0.0001234167,0.00008101255,0.0002241424,0.00003941185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001825879,"about_ca_system_score_gemma":0.0004022531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05033315,"about_ca_topic_score_gemma":0.2586469,"domain_scores_codex":[0.9989001,0.00002521882,0.0003216709,0.0002360471,0.0002244469,0.0002925843],"domain_scores_gemma":[0.9982982,0.0000780081,0.0001750912,0.0004357217,0.00008133175,0.0009316014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006137015,0.00004599764,0.4122644,0.00004577684,0.00005302225,0.0002447503,0.001960498,0.004953158,0.03692137,0.00333316,0.006752845,0.5333636],"study_design_scores_gemma":[0.0007463933,0.000460034,0.5710846,0.00008398372,0.0001206703,0.0002533231,0.0003994566,0.06017477,0.0004246656,0.007470007,0.3578814,0.000900685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08248074,0.000002180202,0.9153908,0.0002185159,0.0003523817,0.0002132197,0.0008894042,0.000002942404,0.0004497987],"genre_scores_gemma":[0.5939872,4.293788e-7,0.4056477,0.0002099986,0.0001028041,0.000001176639,0.00002487177,0.00001048706,0.00001529392],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.532463,"threshold_uncertainty_score":0.9559907,"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."}}