{"id":"W2059768731","doi":"10.1023/b:aire.0000007179.60276.39","title":"Reasoning with Numeric and Symbolic Time Information","year":2003,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Constraint satisfaction problem; Qualitative reasoning; Tabu search; Constraint satisfaction; Constraint satisfaction dual problem; Metric (unit); Constraint (computer-aided design); Scheduling (production processes); Constraint programming; Theoretical computer science; Local consistency; Mathematical optimization; Artificial intelligence; Mathematics; Probabilistic logic","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":[0.0002980414,0.00008794913,0.0001341638,0.00005378712,0.00009601744,0.0001237643,0.0001088923,0.00002125802,0.000164685],"category_scores_gemma":[0.000146627,0.00007174793,0.00002155967,0.0004907919,0.00003872079,0.0008876367,0.00001872336,0.00006333699,0.0004247452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001551757,"about_ca_system_score_gemma":0.00005285338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006270507,"about_ca_topic_score_gemma":0.000002520361,"domain_scores_codex":[0.999286,0.00006430373,0.0002571032,0.0001361283,0.0001275635,0.000128907],"domain_scores_gemma":[0.9995237,0.00003626103,0.0001008391,0.0001910507,0.00008424203,0.00006389013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.074628e-7,0.000005278597,0.00006085897,0.00006757207,0.000003619949,6.734963e-7,0.0001054801,0.00008623574,0.000008962277,0.2166945,0.00004650941,0.7829198],"study_design_scores_gemma":[0.0002374972,0.0007800917,0.002289179,0.01283781,0.0002590333,0.001842632,0.0006449618,0.5057795,0.01476468,0.04048553,0.4167546,0.003324493],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002816195,0.00395194,0.9892372,0.000598441,0.00005718005,0.0002421299,3.716375e-7,0.00008502939,0.005546085],"genre_scores_gemma":[0.5613497,0.1103891,0.3201238,0.007674746,0.00006059862,0.0001250555,0.0000205719,0.00002849582,0.000227996],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7795953,"threshold_uncertainty_score":0.5459382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452773223888117,"score_gpt":0.2480979524556574,"score_spread":0.2335702202167762,"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."}}