{"id":"W1924687241","doi":"10.1109/cca.2003.1223258","title":"Identifying strength of boards using mechanical modeling and a Weibull-based feature","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Weibull distribution; Finite element method; Grading (engineering); Feature (linguistics); Structural engineering; Stress (linguistics); Nondestructive testing; Computer science; Engineering; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001623543,0.00009599397,0.0001636936,0.0000961507,0.00004630639,0.00003096894,0.00003367205,0.0001508244,0.000008894246],"category_scores_gemma":[0.00001737213,0.00008613984,0.00005018132,0.0001389463,0.000006223529,0.00007801344,0.00001258817,0.0001444075,0.000001331558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005507009,"about_ca_system_score_gemma":0.0000204864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001604807,"about_ca_topic_score_gemma":0.00001580731,"domain_scores_codex":[0.9994022,0.00001162466,0.0002042928,0.0001122971,0.0001450748,0.0001245221],"domain_scores_gemma":[0.999778,0.00001303404,0.0000251965,0.0001028054,0.00003375508,0.00004725525],"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":[0.00001392802,0.000009146027,0.00001101872,0.00007597372,0.00002080651,0.00000287068,0.00005808863,0.8812104,0.1165635,0.0004931261,0.00001301737,0.001528072],"study_design_scores_gemma":[0.0006830802,0.00002925954,0.000005683869,0.0001155894,0.00001580353,0.000009657896,0.0001335034,0.899649,0.09903,0.0001935786,0.00003113212,0.0001037562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6127902,0.00007994087,0.3865846,0.000007480407,0.0002197591,0.00007001011,0.000002288227,0.00009775663,0.0001479939],"genre_scores_gemma":[0.9941096,0.000003687729,0.005768555,0.000006850673,0.00008242569,0.00000175388,8.985137e-7,0.00001732928,0.000008863181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3813195,"threshold_uncertainty_score":0.3512681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03420115373527515,"score_gpt":0.2606756275827881,"score_spread":0.2264744738475129,"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."}}