{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004271709,0.001404904,0.0009463251,0.004039797,0.000495835,0.004153482,0.001293232,0.0008740154,0.007630784],"category_scores_gemma":[0.01684938,0.0009171421,0.0009946722,0.007356505,0.002160396,0.003938794,0.002743574,0.002229344,0.002260965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007996945,"about_ca_system_score_gemma":0.000990015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002134859,"about_ca_topic_score_gemma":0.00263444,"domain_scores_codex":[0.9968928,0.001049353,0.0003064552,0.0004699414,0.001208106,0.00007321304],"domain_scores_gemma":[0.993083,0.004983439,0.0001846596,0.0008448815,0.0008317304,0.00007217125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003361271,0.00002031862,0.0009685261,0.0004714182,0.0001106901,0.0001087962,0.0005402306,0.007748031,0.002397781,0.4506904,0.01857236,0.5183378],"study_design_scores_gemma":[0.00001727583,0.00003267997,0.00137395,0.0002479241,0.00006797471,0.0003792262,0.0002850386,0.07104041,0.004576861,0.6862622,0.2356566,0.00005980856],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001429048,0.01121714,0.968142,0.0008901177,0.0005603584,0.0000625687,0.0003014512,0.0007715627,0.01662565],"genre_scores_gemma":[0.0399247,0.01160566,0.9364687,0.0003349454,0.0007956009,0.0001974462,0.0007149586,0.0007343457,0.009223791],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007630784,"threshold_uncertainty_score":0.02552754,"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."}}