{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001200297,0.0001108908,0.0001123564,0.0004937067,0.002359564,0.0004095138,0.0004484162,0.00008353856,0.0002697804],"category_scores_gemma":[0.0001743175,0.0001174213,0.00007036269,0.0008575426,0.0001141907,0.0006270933,0.0003265543,0.0006651841,0.00001319747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000432648,"about_ca_system_score_gemma":0.0004503864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001243164,"about_ca_topic_score_gemma":0.001239084,"domain_scores_codex":[0.9979951,0.0002573572,0.0002650039,0.0004322481,0.0006130506,0.0004371744],"domain_scores_gemma":[0.9988744,0.0001294138,0.00002985441,0.0004388748,0.0004351237,0.00009230787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001984228,0.0001373292,0.0007452103,0.00002473304,0.00003651283,0.000004231737,0.0005554116,0.03715544,0.01448698,0.5523987,0.009093244,0.3853423],"study_design_scores_gemma":[0.0004558395,0.0004374018,0.001166234,0.00001189341,0.000003066013,0.00006169501,0.0007131142,0.9278449,0.001923901,0.004891458,0.0622419,0.0002486202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002434738,0.00006916948,0.9750575,0.01756504,0.0003663786,0.002020215,0.00008901687,0.0003638227,0.002034069],"genre_scores_gemma":[0.8986329,0.00003173847,0.09771974,0.0003486397,0.00002238907,0.002089457,0.00009466734,0.00001502432,0.001045477],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8961982,"threshold_uncertainty_score":0.9989392,"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."}}