{"id":"W7007885591","doi":"","title":"Agent-based manufacturing scheduling: an overview","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006486455,0.0001214772,0.0001073918,0.00006857327,0.0000578661,0.0000517864,0.0001260154,0.00006305039,0.00285509],"category_scores_gemma":[0.00001082391,0.0001276489,0.000045086,0.00009072449,0.00001251554,0.0001071656,0.000008399091,0.0001249639,0.0003080111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003171306,"about_ca_system_score_gemma":0.000003314196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.953438e-7,"about_ca_topic_score_gemma":9.248999e-7,"domain_scores_codex":[0.9993619,0.00001536563,0.0001382977,0.0001413349,0.000137602,0.0002055308],"domain_scores_gemma":[0.9995998,0.00001702409,0.00001519611,0.0002353696,0.00001573224,0.0001168762],"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":[0.000001374999,0.0000411017,0.00004364285,0.0000586032,0.00001649982,0.00001526723,0.0001301447,0.9486747,0.00103324,0.0002184425,0.0004334409,0.04933352],"study_design_scores_gemma":[0.0002695585,0.00001356767,0.00009883683,0.00002301665,0.000008191946,0.000002377908,0.00001916439,0.9813306,0.01501046,0.0001179971,0.002937703,0.000168563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6031235,0.005004496,0.2934184,0.0009284213,0.002590342,0.0004777507,0.00002457887,0.005504104,0.08892835],"genre_scores_gemma":[0.7527079,0.0001902379,0.2463935,0.0002571617,0.0001536693,0.00001107572,0.00001294915,0.00004648744,0.0002269835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1495844,"threshold_uncertainty_score":0.9980564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04809670682625367,"score_gpt":0.2525895905180821,"score_spread":0.2044928836918284,"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."}}