{"id":"W2743001511","doi":"10.1007/s11004-017-9699-5","title":"Which Path to Choose in Sequential Gaussian Simulation","year":2017,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Path (computing); Covariance; Gaussian; Node (physics); Kriging; Realization (probability); Algorithm; Mathematical optimization; Cluster analysis; Data mining; Mathematics; Statistics; Artificial intelligence; Machine learning","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005572286,0.00009170367,0.0001221748,0.00003427177,0.0003456365,0.000243865,0.0004709066,0.00003502772,0.001119281],"category_scores_gemma":[0.0009919878,0.00007273,0.00001921878,0.000171607,0.0001774302,0.0002538814,0.000307427,0.00006525903,0.0009879805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004400191,"about_ca_system_score_gemma":0.00001159009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007177916,"about_ca_topic_score_gemma":0.0009053082,"domain_scores_codex":[0.9988012,0.00002075998,0.0001958008,0.0002948411,0.0003847101,0.0003026517],"domain_scores_gemma":[0.9993708,0.00006879165,0.00007272622,0.0003367279,0.000007614527,0.0001433913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007390825,0.001368256,0.5276408,0.0002541594,0.00001763268,0.0001376264,0.01783353,0.07815687,0.01071578,0.121766,0.002273737,0.2397617],"study_design_scores_gemma":[0.0001594024,0.00004361127,0.6968457,0.00005567769,0.000003847006,0.000002180331,0.0001324116,0.2374083,0.00008576293,0.06417166,0.0008976503,0.0001937971],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874575,0.000001428341,0.03100941,0.001112034,0.0002124538,0.00025986,0.00000534525,0.00002520806,0.07991672],"genre_scores_gemma":[0.9899498,8.335941e-7,0.009115232,0.0001090972,0.00002583617,0.00001428019,6.248596e-7,0.000004531546,0.0007797675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2395679,"threshold_uncertainty_score":0.9997938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013319731057124,"score_gpt":0.3080154306561378,"score_spread":0.2778822333455666,"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."}}