{"id":"W2039338492","doi":"10.1063/1.4912428","title":"Improving the ADACOR2 supervisor holon scheduling mechanism with genetic algorithms","year":2015,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Supervisor; USable; Computer science; Scheduling (production processes); Workstation; Genetic algorithm; Job shop scheduling; Dynamic priority scheduling; Distributed computing; Algorithm; Mathematical optimization; Embedded system; Schedule; Routing (electronic design automation); Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838121,0.0002778098,0.0002094088,0.00009122043,0.00014577,0.0003199219,0.000402879,0.0001237992,0.0000300203],"category_scores_gemma":[0.00008847685,0.0001999744,0.00003736391,0.0003594648,0.00007085651,0.0003149654,0.00006578603,0.0003532372,0.00005783156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006771675,"about_ca_system_score_gemma":0.000109093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004343821,"about_ca_topic_score_gemma":0.000004567578,"domain_scores_codex":[0.9986188,0.000007569633,0.0002460089,0.000324744,0.0003720571,0.0004307896],"domain_scores_gemma":[0.9990305,0.00002423094,0.00006365321,0.0001695139,0.0004968283,0.0002152591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005430159,0.0006324153,0.04280405,0.001896462,0.001472959,0.0002030279,0.1245857,0.2731137,0.1232117,0.04777057,0.002959008,0.3808074],"study_design_scores_gemma":[0.0005461557,0.0001359336,0.0001746849,0.00005977939,0.00004079843,0.0000580971,0.003756392,0.9905497,0.003601643,0.0006317944,0.0001046412,0.0003404053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3024476,0.00036085,0.6937311,0.0003787391,0.0003900149,0.0003408884,0.000003709639,0.0008479911,0.001499158],"genre_scores_gemma":[0.7972635,0.0000415362,0.2021411,0.0001308257,0.0001922657,0.00006754527,0.000003994471,0.00005911678,0.000100121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.717436,"threshold_uncertainty_score":0.8154718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02499708961358461,"score_gpt":0.216465580299274,"score_spread":0.1914684906856894,"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."}}