{"id":"W4225962937","doi":"10.1007/s43069-021-00116-6","title":"Nature-Inspired Techniques for Dynamic Constraint Satisfaction Problems","year":2022,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Mathematical optimization; Computer science; Constraint (computer-aided design); Scheduling (production processes); Set (abstract data type); Constraint satisfaction; Context (archaeology); Optimization problem; Sequence (biology); Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001101743,0.0009221731,0.001074106,0.00111316,0.0007023769,0.001197532,0.001780695,0.001332674,0.003793466],"category_scores_gemma":[0.002769955,0.000641813,0.00135902,0.002027951,0.001109389,0.001326646,0.001736184,0.002784598,0.0004428759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083041,"about_ca_system_score_gemma":0.001104442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003157869,"about_ca_topic_score_gemma":0.004257515,"domain_scores_codex":[0.9991937,0.0002569946,0.00003193648,0.000099866,0.0003582603,0.00005923211],"domain_scores_gemma":[0.9989597,0.0007258757,0.00007361308,0.00009249127,0.0001132355,0.00003514374],"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":[0.00004608939,0.0001021379,0.0003533795,0.0003226046,0.000118481,0.0001029828,0.0001554786,0.5993103,0.00301144,0.2653705,0.006753016,0.1243536],"study_design_scores_gemma":[0.00001498203,0.00002062272,0.00007290084,0.00002540703,0.00001237245,0.00003499779,0.00001753305,0.9050984,0.0004337711,0.0881948,0.006066462,0.000007711882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004140479,0.0009644381,0.9857245,0.0004410326,0.0001152705,0.00005422391,0.00005512091,0.00009004116,0.008414912],"genre_scores_gemma":[0.1788969,0.00261503,0.8051443,0.0004381613,0.0003091246,0.000405557,0.0003104914,0.0002247067,0.01165564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003793466,"threshold_uncertainty_score":0.01269042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02781063245877669,"score_gpt":0.3492063004535139,"score_spread":0.3213956679947372,"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."}}