{"id":"W7145955814","doi":"","title":"信号制御の最適化におけるメタ戦略の比較と制御パラメータの連続自動調整への適用","year":2006,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Internet of Things and Social Network Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Offset (computer science); Metaheuristic; Genetic algorithm; Simulated annealing; Optimal control; Control point; Control (management)","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005312718,0.0006490317,0.0005455543,0.0002488488,0.002331173,0.00201175,0.002246717,0.0003531701,0.0002695165],"category_scores_gemma":[0.0004098854,0.0006879044,0.0004530914,0.0008489746,0.0007660866,0.003744122,0.00169639,0.0009541478,0.0009700461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008253103,"about_ca_system_score_gemma":0.0009638182,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009872085,"about_ca_topic_score_gemma":0.0006485677,"domain_scores_codex":[0.9948642,0.0001969449,0.001208819,0.001263561,0.001477309,0.0009891171],"domain_scores_gemma":[0.9963813,0.000299898,0.0005517362,0.00164347,0.0008159166,0.000307718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008976817,0.0006144496,0.0007159338,0.00008256046,0.0001721509,0.0009601588,0.0004456625,0.00118784,0.002073925,0.7853864,0.2074849,0.000786213],"study_design_scores_gemma":[0.0007726411,0.0001683614,0.002226107,0.0007901724,0.0001329941,0.0008693234,0.0001271744,0.01760317,0.00390571,0.005143494,0.9671125,0.001148364],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08179118,0.007096871,0.3007306,0.01315278,0.1277668,0.001503944,0.002223084,0.00161645,0.4641183],"genre_scores_gemma":[0.9484934,0.0001318229,0.01625354,0.0007608258,0.01427524,0.00006347076,0.0007711067,0.00004936907,0.01920121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8667023,"threshold_uncertainty_score":0.9998078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213788230153902,"score_gpt":0.2505709440155723,"score_spread":0.2384330617140333,"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."}}