{"id":"W2031851532","doi":"10.1007/s11053-010-9120-2","title":"Multiple Point Metrics to Assess Categorical Variable Models","year":2010,"lang":"en","type":"article","venue":"Natural Resources Research","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Variogram; Categorical variable; Geostatistics; Statistics; Mathematics; Histogram; Spatial analysis; Consistency (knowledge bases); Function (biology); Data mining; Point (geometry); Computer science; Kriging; Image (mathematics); Spatial variability; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001865364,0.0001283503,0.000148627,0.0002267843,0.000381209,0.0001963398,0.0006894998,0.0001336633,0.0008474539],"category_scores_gemma":[0.003415979,0.0001049711,0.00003778904,0.001803069,0.0001702786,0.0001827071,0.0009689608,0.001382823,0.0007314336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001795752,"about_ca_system_score_gemma":0.00002660164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00433661,"about_ca_topic_score_gemma":0.000608845,"domain_scores_codex":[0.9969159,0.0001252701,0.0001913308,0.0004706528,0.001516565,0.0007802229],"domain_scores_gemma":[0.9979479,0.001114152,0.00002765855,0.0004066573,0.0001065494,0.0003970734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004194902,0.0006561546,0.0481477,0.00008100203,0.00007260162,0.0002357408,0.005404785,0.0253338,0.4545365,0.04679238,0.1802674,0.2380525],"study_design_scores_gemma":[0.0005558613,0.0001703139,0.03059961,0.00001016288,0.00000722892,0.00002353103,0.0004374833,0.4071759,0.002705134,0.02605672,0.5317721,0.0004859691],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9168382,0.00009219781,0.007593631,0.0009029444,0.0004786309,0.0005236421,0.00001719749,0.00007502594,0.07347854],"genre_scores_gemma":[0.9759395,0.000008855494,0.01924418,0.0001697272,0.0001196609,0.00003506145,0.000007714338,0.0000199739,0.004455322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4518313,"threshold_uncertainty_score":0.9401343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08277709934552492,"score_gpt":0.353729544129123,"score_spread":0.2709524447835981,"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."}}