{"id":"W2737547726","doi":"10.1144/petgeo2016-161","title":"Horizontal variogram inference in the presence of widely spaced well data","year":2017,"lang":"en","type":"article","venue":"Petroleum Geoscience","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Variogram; Geology; Inference; Environmental geology; Geobiology; Metamorphic petrology; Telmatology; Economic geology; Geostatistics; Hydrogeology; Gemology; Igneous petrology; Palaeogeography; Engineering geology; Geodesy; Regional geology; Seismology; Volcanism; Statistics; Computer science; Mathematics; Geotechnical engineering; Artificial intelligence; Kriging; Spatial variability","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007754147,0.000630133,0.001124792,0.001829584,0.0005242849,0.001543293,0.001322523,0.0009569797,0.001467968],"category_scores_gemma":[0.03516905,0.0007590898,0.0009250909,0.002180057,0.001321448,0.002407233,0.001988943,0.001725115,0.0002591165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006999183,"about_ca_system_score_gemma":0.001125859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005640411,"about_ca_topic_score_gemma":0.005705139,"domain_scores_codex":[0.9966037,0.001764032,0.0001867794,0.0006461592,0.0005736977,0.0002255479],"domain_scores_gemma":[0.9704723,0.0249031,0.001345379,0.002036703,0.001012025,0.0002305584],"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":[0.0002722807,0.00006315929,0.01730927,0.0001585054,0.0002908529,0.0003169535,0.0002015277,0.7872456,0.004752132,0.0597367,0.001099296,0.1285537],"study_design_scores_gemma":[0.00001086486,0.00002938245,0.002972358,0.00002320179,0.00001952788,0.0000547367,0.00004344929,0.9373447,0.002442672,0.0561072,0.000924683,0.00002713941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04311947,0.0001836811,0.9556338,0.0001044773,0.00001246825,0.00001747895,0.0001825896,0.0002195438,0.0005264441],"genre_scores_gemma":[0.688819,0.0003706433,0.3081244,0.00009851906,0.00006335441,0.00008317747,0.001174377,0.0002167174,0.001049733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007754147,"threshold_uncertainty_score":0.04100835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03681143445189861,"score_gpt":0.2943507580667212,"score_spread":0.2575393236148226,"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."}}