{"id":"W2896174059","doi":"10.1139/cgj-2017-0709","title":"A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data","year":2018,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Pattern recognition (psychology); Bayesian probability; Probabilistic logic; Artificial intelligence; Data mining; Python (programming language); Feature vector; Stratification (seeds); MATLAB; Cone penetration test; Mathematics; Geology; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001403325,0.0006781071,0.0007957481,0.002898699,0.0004142152,0.0009935806,0.001628529,0.0007617873,0.0007515025],"category_scores_gemma":[0.004427863,0.0004592311,0.0009059962,0.001963666,0.0007210235,0.001239264,0.001147147,0.001048388,0.0003904248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006491767,"about_ca_system_score_gemma":0.001263215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006902884,"about_ca_topic_score_gemma":0.01274021,"domain_scores_codex":[0.9991199,0.0002255994,0.00006292553,0.0002642597,0.0002574346,0.0000699151],"domain_scores_gemma":[0.9984373,0.0007057955,0.0002290857,0.0002160387,0.0003519694,0.00005969814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001589688,0.000339686,0.02087828,0.0002050127,0.0002249304,0.0002634902,0.0003831466,0.444789,0.01259255,0.02605221,0.004027081,0.4900857],"study_design_scores_gemma":[0.000007299192,0.00002124597,0.002128439,0.00001113969,0.00001072563,0.00004729839,0.00002948728,0.98369,0.001097913,0.01195339,0.0009872512,0.00001593005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01208272,0.00007416464,0.9864136,0.00007904804,0.000007300915,0.0000505729,0.0002344127,0.0004202524,0.0006380299],"genre_scores_gemma":[0.4323772,0.0002323047,0.5624993,0.0001773718,0.00009703074,0.000296458,0.002176455,0.000150047,0.001993774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006902884,"threshold_uncertainty_score":0.0137254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05501350137834433,"score_gpt":0.2800192973503038,"score_spread":0.2250057959719595,"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."}}