{"id":"W2046000332","doi":"10.1023/b:narr.0000046916.91703.bb","title":"Minimum Acceptance Criteria for Geostatistical Realizations","year":2004,"lang":"en","type":"article","venue":"Natural Resources Research","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":133,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Kriging; Computer science; Geostatistics; Software; Measure (data warehouse); Data mining; Mathematical optimization; Industrial engineering; Statistics; Mathematics; Engineering; Machine learning; Spatial variability","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006646066,0.0001044183,0.0001144729,0.00007631261,0.000500788,0.0001317543,0.0003690592,0.00007817453,0.0008465036],"category_scores_gemma":[0.001966419,0.00009228134,0.00004029225,0.000468741,0.0004074302,0.0001069577,0.0002528138,0.0003094113,0.0002647039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002296811,"about_ca_system_score_gemma":0.00002598513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007082908,"about_ca_topic_score_gemma":0.0003785362,"domain_scores_codex":[0.9980527,0.00007911472,0.0001882391,0.0003703286,0.0007012714,0.0006083529],"domain_scores_gemma":[0.9989182,0.0005603158,0.00002940483,0.0002386086,0.00008367829,0.0001697743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009355609,0.0006159709,0.008157976,0.0002710332,0.00008770217,0.000175247,0.0115905,0.003881741,0.05192913,0.1394687,0.4748699,0.3080165],"study_design_scores_gemma":[0.001525935,0.000315563,0.07906249,0.00006511033,0.00001178769,0.00001685008,0.0007267004,0.0138072,0.0009388036,0.07343212,0.8296649,0.00043258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8758513,0.000665586,0.04842769,0.00642379,0.0006658681,0.001947424,0.0003873291,0.0001705353,0.06546044],"genre_scores_gemma":[0.9751707,0.00003422439,0.01994178,0.0001853151,0.0001408197,0.00008810895,0.00005538111,0.00002104508,0.004362644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3547949,"threshold_uncertainty_score":0.9268621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05822061189395763,"score_gpt":0.3977040138884395,"score_spread":0.3394834019944819,"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."}}