{"id":"W138014484","doi":"10.1007/978-94-007-4153-9_14","title":"Applications of Data Coherency for Data Analysis and Geological Zonation","year":2012,"lang":"en","type":"book-chapter","venue":"Quantitative geology and geostatistics","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Facies; Cluster analysis; Data mining; Geology; Similarity (geometry); Metric (unit); Quality (philosophy); Data quality; Measure (data warehouse); Computer science; Image (mathematics); Artificial intelligence; Engineering; Paleontology","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":[],"category_scores_codex":[0.0006736767,0.0002237292,0.0006320712,0.0002220456,0.0001765488,0.00001582364,0.0004759772,0.000316263,0.0009885591],"category_scores_gemma":[0.0003120102,0.0001794497,0.00004357884,0.0001042574,0.0006534031,0.0001288292,0.0001816887,0.0001995131,0.00003135362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001137435,"about_ca_system_score_gemma":0.00003362987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008394165,"about_ca_topic_score_gemma":0.004693464,"domain_scores_codex":[0.9984215,0.00006312523,0.0004441269,0.0007064519,0.0001392887,0.0002255434],"domain_scores_gemma":[0.9967711,0.001883021,0.0003863922,0.0006949249,0.0001592355,0.0001053442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001002371,0.00003953985,0.1520651,0.0001617924,0.002090398,0.000004067434,0.0001056106,0.0003552807,2.288348e-7,0.4819113,0.001256988,0.3619094],"study_design_scores_gemma":[0.0003251475,0.0004415402,0.0703538,0.00001709045,0.005742264,0.000008400965,0.00008724229,0.5059522,1.089897e-7,0.3335965,0.08299994,0.0004757369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003402601,0.01745399,0.9513258,0.0003468423,0.0000490957,0.0004039925,0.02448307,0.00001990984,0.005577024],"genre_scores_gemma":[0.2124174,0.01295112,0.5542159,0.000226611,0.0001896905,0.000011763,0.1979622,0.00001441619,0.02201087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5055969,"threshold_uncertainty_score":0.9999247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1270774966883502,"score_gpt":0.3297860696180339,"score_spread":0.2027085729296837,"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."}}