{"id":"W4312286014","doi":"10.1016/j.ifacol.2022.09.493","title":"Towards intelligent manufacturing system safety strategies: generating LockOut/TagOut sheets by Machine Learning – a case study","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; École de Technologie Supérieure","funders":"Mitacs","keywords":"Computer science; Similarity (geometry); Work (physics); Manufacturing; Artificial intelligence; Industrial engineering; 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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005688748,0.0005279462,0.0009614613,0.0004601002,0.002690519,0.0006824419,0.001195797,0.00009617518,0.002814981],"category_scores_gemma":[0.0004305954,0.0004207415,0.0004559729,0.001104116,0.00007430716,0.0004203059,0.0008837611,0.001099441,0.0001242909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005056144,"about_ca_system_score_gemma":0.0002217026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005119762,"about_ca_topic_score_gemma":0.001847385,"domain_scores_codex":[0.9916747,0.001656999,0.001824377,0.001353709,0.002785267,0.0007049657],"domain_scores_gemma":[0.997133,0.000739986,0.0006489625,0.0009615339,0.0001907538,0.0003257662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002703007,0.0007783865,0.004419671,0.00004000083,0.0005220512,0.008215277,0.02743339,0.6559034,0.001484417,0.00007793069,0.00006900883,0.3007862],"study_design_scores_gemma":[0.001001687,0.0005979431,0.0001645733,0.00001828157,0.000184882,0.001563049,0.5538256,0.4349899,0.0003704679,0.00004345714,0.006583508,0.0006566744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720656,0.001365171,0.0220945,0.0008246743,0.0004997111,0.0006412903,0.0003219505,0.0002624115,0.001924675],"genre_scores_gemma":[0.9854001,0.00007211816,0.0111935,0.0001512338,0.0002349363,0.00006337207,0.0001142733,0.00005362678,0.002716865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5263922,"threshold_uncertainty_score":0.9998245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05042399439626334,"score_gpt":0.3411584891139613,"score_spread":0.290734494717698,"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."}}