{"id":"W2783796426","doi":"10.1109/icmla.2017.0-124","title":"Incremental Dynamic Search Solver","year":2017,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Solver; Constraint satisfaction problem; Computer science; Usability; Robustness (evolution); Constraint satisfaction; Local search (optimization); Extension (predicate logic); Mathematical optimization; Constraint (computer-aided design); Guided Local Search; Problem solver; Theoretical computer science; Algorithm; Computational science; Mathematics; Programming language; 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":[0.0008563742,0.001059857,0.001176416,0.00102142,0.0005850075,0.001758194,0.00310721,0.0014493,0.01637433],"category_scores_gemma":[0.003447883,0.0005534025,0.00117019,0.001331026,0.0007777424,0.001905424,0.002643955,0.00193404,0.003105708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123402,"about_ca_system_score_gemma":0.002650967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003982454,"about_ca_topic_score_gemma":0.006414239,"domain_scores_codex":[0.9990006,0.0001994183,0.00006236495,0.00023853,0.000357063,0.0001420508],"domain_scores_gemma":[0.9989176,0.0005307918,0.00006607139,0.0001699467,0.0002522045,0.00006336741],"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":[0.000207012,0.0001574454,0.0009484558,0.0006570641,0.0001199064,0.0005189209,0.0001952072,0.5197077,0.006753447,0.2404176,0.02062773,0.2096895],"study_design_scores_gemma":[0.00004805206,0.00002649423,0.00005686723,0.00002851547,0.00002611018,0.0001194889,0.00003677171,0.9474145,0.002712025,0.03184989,0.01766759,0.00001368634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004189086,0.0002849911,0.9744504,0.0002861535,0.00009525799,0.0001416057,0.0003906721,0.001836399,0.01832546],"genre_scores_gemma":[0.1513321,0.0005650685,0.8293101,0.0004010265,0.00009430854,0.0005666748,0.001492468,0.0008590764,0.01537917],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01637433,"threshold_uncertainty_score":0.05477756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198134027213537,"score_gpt":0.2911310338077943,"score_spread":0.2713176310864406,"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."}}