{"id":"W2995476185","doi":"10.1080/17499518.2019.1700423","title":"The story of statistics in geotechnical engineering","year":2019,"lang":"en","type":"article","venue":"Georisk Assessment and Management of Risk for Engineered Systems and Geohazards","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geotechnical engineering; Geotechnical investigation; Engineering; Geology; Statistics; Forensic engineering; Civil engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009648107,0.0001971719,0.0004166936,0.0001640803,0.00004283586,0.00002716639,0.0001323393,0.0000912791,0.000001378027],"category_scores_gemma":[0.00001951952,0.0001624437,0.00006785582,0.0001935114,0.00002820404,0.00005267702,0.00005721215,0.0002415305,4.010093e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003489705,"about_ca_system_score_gemma":0.00000699778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004786563,"about_ca_topic_score_gemma":0.000004393007,"domain_scores_codex":[0.9988624,0.00002099997,0.0004461483,0.000183208,0.0002098078,0.0002773608],"domain_scores_gemma":[0.9992101,0.000343923,0.0000776438,0.0002691516,0.00004335125,0.00005583597],"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":[0.000008493652,0.00001701871,0.0007086048,0.001984777,0.0002620874,0.000001335197,0.00003312751,0.9453916,0.0001286557,0.03758378,0.0001609863,0.01371956],"study_design_scores_gemma":[0.0007556883,0.00008253851,0.02828174,0.00021528,0.0001294738,0.000001179926,0.000358484,0.9596943,0.00002400571,0.0001567484,0.01008847,0.0002121126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5103129,0.006978641,0.4801755,0.00003159178,0.0007660078,0.001149624,0.000264318,0.0001794072,0.0001420193],"genre_scores_gemma":[0.9868984,0.008312639,0.004544666,4.820105e-7,0.00002153934,0.00008503604,0.00001527248,0.00002766113,0.00009431887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4765855,"threshold_uncertainty_score":0.6624261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003893565329926218,"score_gpt":0.2152059095361201,"score_spread":0.2113123442061939,"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."}}