{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002984192,0.0001006948,0.0001415638,0.0003869784,0.00009732154,0.0001339755,0.00002490447,0.0001215826,0.00002420452],"category_scores_gemma":[0.00001897881,0.00009953476,0.0001587168,0.0003745839,0.000008525422,0.0002111902,0.000003862555,0.00009239616,0.0000179451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002105759,"about_ca_system_score_gemma":0.000009988922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001053784,"about_ca_topic_score_gemma":0.00005717291,"domain_scores_codex":[0.9993601,0.00002345723,0.0002316625,0.0001440407,0.0001081725,0.0001325013],"domain_scores_gemma":[0.9997575,0.00006496487,0.00002587599,0.00009730901,0.00002573103,0.00002860883],"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":[0.00001435398,0.000009394302,0.00008506454,0.0005568262,0.001214967,0.000003843445,0.000790178,0.4007953,0.2402449,0.00024614,0.0001471607,0.3558918],"study_design_scores_gemma":[0.00008147916,0.00001585622,0.0007216097,0.00003165522,0.0001776781,0.00001026791,0.00003416385,0.803164,0.1946132,0.0001060298,0.0009461595,0.00009786846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5665519,0.00002846642,0.4314106,0.000002668745,0.0009608107,0.0001746166,0.000009857022,0.0006476101,0.0002134926],"genre_scores_gemma":[0.9991554,0.000001429991,0.0005439011,0.000002858948,0.0002204261,0.00002909776,0.00001087392,0.00001825051,0.0000177776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4326034,"threshold_uncertainty_score":0.405891,"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."}}