{"id":"W2000989622","doi":"10.1115/detc2004-57646","title":"A Constraint Satisfaction Problem in Real-Time Collaborative Assembly Modeling","year":2004,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint satisfaction problem; Local consistency; Constraint satisfaction; Computer science; Constraint (computer-aided design); Context (archaeology); Constraint satisfaction dual problem; Computation; Constraint graph; Distributed computing; Constraint logic programming; Consistency (knowledge bases); Protocol (science); Process (computing); Constraint programming; Hybrid algorithm (constraint satisfaction); Mathematical optimization; Algorithm; Artificial intelligence; Engineering; Programming language; Mathematics","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":[0.00006317141,0.00009122531,0.00009826611,0.00007871974,0.00002467529,0.00003510207,0.00002809049,0.00005777817,0.00006072473],"category_scores_gemma":[0.000004695238,0.00008912828,0.00001159522,0.0001611874,0.000006901066,0.0001795968,0.000005942986,0.00007056427,0.00002283309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001270721,"about_ca_system_score_gemma":0.00003635289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002956289,"about_ca_topic_score_gemma":0.0002459605,"domain_scores_codex":[0.9995182,0.00000570762,0.0001605166,0.0001082162,0.00007477518,0.0001325823],"domain_scores_gemma":[0.9998522,0.000007162442,0.0000141369,0.00005897886,0.00003694346,0.00003060527],"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":[0.000002827094,0.000005487246,0.0000479361,0.00002843592,0.000005317429,0.000001534785,0.0003416345,0.9954292,0.001460703,0.001138822,0.000008997892,0.001529162],"study_design_scores_gemma":[0.0004633468,0.0000172341,0.0003469677,0.00005244691,0.000003462725,0.000002176894,0.0001496621,0.9900032,0.00716626,0.001629664,0.00001163746,0.0001539968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6017478,0.00002943335,0.3452328,0.00008149169,0.00005807367,0.0003246486,0.000003871175,0.0005529707,0.05196893],"genre_scores_gemma":[0.9589643,0.0001018405,0.04082719,0.000008565797,0.00001303425,0.00002254765,0.000006454185,0.00001496001,0.00004104087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3572166,"threshold_uncertainty_score":0.3634546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00860980822514614,"score_gpt":0.2164014684308102,"score_spread":0.2077916602056641,"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."}}