{"id":"W3192289992","doi":"10.1111/coin.12438","title":"Improving stochastic local search for uniform <scp><i>k</i>‐SAT</scp> by generating appropriate initial assignment","year":2021,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Heuristics; Random variable; Local search (optimization); Heuristic; Algorithm; Computer science; Variable (mathematics); Mathematical optimization; Mathematics; Statistics","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":[0.000402928,0.0001813559,0.000162767,0.00009410284,0.0003992516,0.0003629157,0.0003514187,0.00007811813,0.00003126563],"category_scores_gemma":[0.0002619752,0.000199479,0.00008438185,0.0003995037,0.00009982516,0.0004116421,0.0002065304,0.0002194452,0.00003837209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001554833,"about_ca_system_score_gemma":0.0005115766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001288908,"about_ca_topic_score_gemma":0.000005849084,"domain_scores_codex":[0.9981111,0.00009401964,0.0004298317,0.0005481318,0.0004713176,0.0003455884],"domain_scores_gemma":[0.9978796,0.001035114,0.0001293826,0.0002088037,0.0005986519,0.0001484252],"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":[0.000001557899,0.00003973649,0.00001755645,0.00001530305,0.00001469927,0.000003623098,0.0003903099,0.8004161,0.0007002324,0.01581105,0.0001615143,0.1824284],"study_design_scores_gemma":[0.0001646383,0.00006197656,0.0000325316,0.00002048944,0.000006550403,0.00004124667,0.0004135949,0.9799973,0.01456772,0.004464034,0.0001263098,0.0001035704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002357625,0.0001105485,0.9961988,0.0002344249,0.0004770075,0.0002915651,0.00003184843,0.0001218798,0.0001763488],"genre_scores_gemma":[0.686659,0.000003264221,0.3124993,0.0003765535,0.00009480875,0.00004128199,0.0001690617,0.00001274284,0.0001439495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6843014,"threshold_uncertainty_score":0.8134517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984284784506302,"score_gpt":0.2886217986190427,"score_spread":0.2587789507739797,"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."}}