{"id":"W2988663399","doi":"10.1109/imtc.2002.1007124","title":"Implementation of a mechanics-based system for estimating the strength of a board","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Finite element method; Stress (linguistics); Grading (engineering); Structural engineering; Size effect on structural strength; Compressive strength; Strength of materials; Feature (linguistics); Computer science; Engineering; Materials science; Composite material","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":[0.0004642345,0.0006069239,0.0004310593,0.0006886172,0.0003363946,0.0007420972,0.001213049,0.0005988617,0.006455759],"category_scores_gemma":[0.00148187,0.0003143396,0.0001821478,0.0003760592,0.0002575598,0.0007115524,0.0003301738,0.0004734385,0.002013049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003919082,"about_ca_system_score_gemma":0.0007604825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002083952,"about_ca_topic_score_gemma":0.002119409,"domain_scores_codex":[0.999683,0.00003299498,0.00002283634,0.0001040347,0.0001325384,0.00002459271],"domain_scores_gemma":[0.9993305,0.0001585435,0.00005843569,0.0001105471,0.0003007405,0.00004123173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003731578,0.0002997635,0.008287451,0.0001433695,0.000069323,0.0001603241,0.0001738698,0.01423909,0.2530316,0.003690513,0.005577787,0.7139538],"study_design_scores_gemma":[0.0001389807,0.001183788,0.01555733,0.000043194,0.0001267152,0.0007783579,0.0001029509,0.5991464,0.3491766,0.00279351,0.0308682,0.00008394997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04045529,0.00005710866,0.9405738,0.00007793458,0.0000564359,0.00039135,0.0001624745,0.01518871,0.003036929],"genre_scores_gemma":[0.2539771,0.00007609297,0.7397642,0.0001032001,0.00003393495,0.0003644032,0.000404788,0.0002241964,0.005052066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006455759,"threshold_uncertainty_score":0.02159661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210602585142759,"score_gpt":0.2737144850036443,"score_spread":0.2516084591522167,"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."}}