{"id":"W2120087804","doi":"10.1139/t11-069","title":"Diagnosis of embankment dam distresses using Bayesian networks. Part I. Global-level characteristics based on a dam distress database","year":2011,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian network; Embankment dam; Levee; Bayesian probability; Engineering; Database; Forensic engineering; Data mining; Computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002254365,0.000296003,0.000361506,0.0001440381,0.0001303978,0.00005604033,0.000428538,0.0001794223,0.0002223062],"category_scores_gemma":[0.0001181532,0.0002964439,0.0001400773,0.0002888577,0.00008131823,0.0001081691,0.00003175417,0.0005360618,0.000004763928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005381631,"about_ca_system_score_gemma":0.0001733153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002535766,"about_ca_topic_score_gemma":0.001616855,"domain_scores_codex":[0.9982859,0.00003663522,0.0005536775,0.0002139158,0.0002573926,0.0006524074],"domain_scores_gemma":[0.9982876,0.0000566694,0.00009702356,0.0004921656,0.0000653893,0.001001133],"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":[0.00004189503,0.0001659617,0.008394603,0.0001612623,0.0001311036,0.0007732894,0.00003012327,0.9559233,0.00003227926,0.0003062647,0.005473392,0.02856658],"study_design_scores_gemma":[0.0004510529,0.0000917927,0.04825208,0.0009610865,0.0001130225,0.0001104312,0.00001817032,0.9407308,0.0001897506,0.00004366905,0.008468792,0.0005693597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1261299,0.0007619514,0.8575532,0.0002641153,0.003240204,0.0004921666,0.009705323,0.0004206356,0.001432512],"genre_scores_gemma":[0.9971658,0.0002007136,0.002104554,0.00008903113,0.0002500864,0.00001326318,0.0001241777,0.00004747839,0.000004873603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.871036,"threshold_uncertainty_score":0.9999487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0304191155228594,"score_gpt":0.2227414280959319,"score_spread":0.1923223125730725,"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."}}