{"id":"W4403869526","doi":"10.3390/automation5040031","title":"Capacity Constraint Analysis Using Object Detection for Smart Manufacturing","year":2024,"lang":"en","type":"article","venue":"Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ontario Centre of Innovation; University of Windsor","keywords":"Constraint (computer-aided design); Computer science; Object (grammar); Artificial intelligence; Manufacturing engineering; Computer vision; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005476904,0.0006825475,0.0006000907,0.001819589,0.0002732479,0.0008506516,0.001015489,0.000584272,0.001460478],"category_scores_gemma":[0.001662639,0.0003636651,0.0005173097,0.001157991,0.0004423013,0.001409939,0.001039345,0.0004700049,0.0003769198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179807,"about_ca_system_score_gemma":0.001018793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008627824,"about_ca_topic_score_gemma":0.007101766,"domain_scores_codex":[0.9995784,0.00003868265,0.00002132674,0.0001191335,0.0001533787,0.00008902336],"domain_scores_gemma":[0.9993318,0.0002095525,0.0001781686,0.0001000791,0.0001418128,0.00003854468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002411567,0.0001758757,0.01597343,0.0001947841,0.00008463905,0.0004307822,0.0001426178,0.4904171,0.03957096,0.006766848,0.002705877,0.4432959],"study_design_scores_gemma":[0.000003675401,0.00002494317,0.002338195,0.000006821166,0.000008123124,0.00004436046,0.00002354484,0.9841444,0.01010828,0.002204606,0.001081535,0.00001153921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08950275,0.0003506195,0.9060597,0.000107566,0.00002291721,0.00005639329,0.0002709145,0.001901908,0.001727214],"genre_scores_gemma":[0.7940879,0.0002406907,0.2034607,0.00007688434,0.0000180579,0.00006948955,0.0005776235,0.0001062337,0.001362426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008627824,"threshold_uncertainty_score":0.01715523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03165001967348346,"score_gpt":0.2559975148347959,"score_spread":0.2243474951613125,"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."}}