{"id":"W2163770137","doi":"10.1109/ictai.2005.77","title":"Improving Lotos simulation using constraint propagation","year":2005,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Constraint (computer-aided design); Theoretical computer science; Local consistency; Combinatorial explosion; Distributed computing; Parallel computing; Programming language; Constraint satisfaction problem; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001318547,0.00007243342,0.0000576619,0.00009057859,0.0001083413,0.0001373113,0.0001049235,0.00003849433,0.0001435412],"category_scores_gemma":[0.00003711334,0.00006961636,0.00002657764,0.0001807926,0.00002446653,0.0009405129,0.00003767058,0.00005234369,0.00003711171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008482236,"about_ca_system_score_gemma":0.00007431421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001699488,"about_ca_topic_score_gemma":0.00001547201,"domain_scores_codex":[0.9993334,0.00002520746,0.000183024,0.0001930208,0.0001432693,0.0001220601],"domain_scores_gemma":[0.9995829,0.00003411555,0.00008132419,0.0001626028,0.0000920966,0.00004700044],"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":[8.56662e-7,0.000009702009,0.0001952848,0.000002408574,0.000001958456,3.513264e-7,0.00009515402,0.5412831,0.003420975,0.01366103,0.000004511355,0.4413246],"study_design_scores_gemma":[0.0001744262,0.000009368959,0.0004622293,0.000004131522,0.000002259878,0.000009933314,0.0000178249,0.9962351,0.002675244,0.00007755146,0.000237225,0.00009468559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01336506,0.000004182672,0.9835469,0.0003945538,0.0001072387,0.0001864823,3.400458e-7,0.000253463,0.002141782],"genre_scores_gemma":[0.7221294,4.241778e-7,0.2775601,0.0001912883,0.00004752868,0.000001771,9.719465e-7,0.000003006208,0.00006549284],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7087644,"threshold_uncertainty_score":0.2838873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246976209853071,"score_gpt":0.2657509892373411,"score_spread":0.2432812271388104,"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."}}