{"id":"W1964031244","doi":"10.1080/10962247.2012.739584","title":"A coupled factorial-analysis-based interval programming approach and its application to air quality management","year":2012,"lang":"en","type":"article","venue":"Journal of the Air & Waste Management Association","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Ministry of Education, India; Ministry of Earth Sciences","keywords":"Interval (graph theory); Factorial; Decision quality; Function (biology); Linear programming; Quality (philosophy); Computer science; Mathematical optimization; Factorial experiment; Air quality index; Operations research; Interval arithmetic; Risk analysis (engineering); Mathematics; Machine learning","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":[0.001759643,0.0001862803,0.0003060435,0.0004137166,0.0001080002,0.00008341425,0.0003188436,0.00006184734,0.000004154926],"category_scores_gemma":[0.00002333474,0.0001491026,0.0002219775,0.00085182,0.000005570365,0.0003485028,0.0001548277,0.0001409989,0.000006991657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005304,"about_ca_system_score_gemma":0.000002015515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004094341,"about_ca_topic_score_gemma":0.000004669014,"domain_scores_codex":[0.9981371,0.0001265108,0.0005961698,0.000144408,0.0006828531,0.0003129723],"domain_scores_gemma":[0.9990175,0.00003067547,0.0005357905,0.0002253573,0.00009773527,0.00009295972],"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":[0.00005480651,0.0001489665,0.02871475,0.0003769919,0.003038843,5.695365e-7,0.000857121,0.9591483,0.00008686448,0.0008652233,0.0008534463,0.005854109],"study_design_scores_gemma":[0.00306961,0.0001060194,0.2445028,0.0001383031,0.007138605,8.907374e-7,0.002190091,0.7049739,0.0007368656,0.0001296086,0.03618777,0.0008255533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3955226,0.0001610431,0.5974476,0.0008499125,0.0009527296,0.001821668,0.000004010302,0.0001376756,0.003102849],"genre_scores_gemma":[0.9944694,0.00002034959,0.004208714,0.0001265109,0.0002775798,0.00005131315,0.00001640071,0.00002424633,0.0008055091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5989468,"threshold_uncertainty_score":0.6080226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156279676059229,"score_gpt":0.2332136399810252,"score_spread":0.2216508432204329,"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."}}