{"id":"W3196451653","doi":"10.1016/j.resconrec.2021.105882","title":"Development of a multi-factorial enviro-economic analysis model for assessing the interactive effects of combined air pollution control policies","year":2021,"lang":"en","type":"article","venue":"Resources Conservation and Recycling","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada","keywords":"Factorial analysis; Factorial; Pollution; Factorial experiment; Control (management); Air pollution; Environmental science; Environmental planning; Computer science; Statistics; Mathematics; Chemistry; Artificial intelligence; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003112581,0.00009131046,0.0002217145,0.00003941259,0.0001756459,0.00001937331,0.00006199858,0.00005176094,0.00001188188],"category_scores_gemma":[0.0001568199,0.00007017348,0.00009470468,0.0001063012,0.0001750961,0.0001438885,0.00006302678,0.00005528922,4.104416e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001632508,"about_ca_system_score_gemma":0.00002720307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002025834,"about_ca_topic_score_gemma":0.0001642796,"domain_scores_codex":[0.9991987,0.00009859225,0.0003245836,0.000160352,0.00009981989,0.0001179911],"domain_scores_gemma":[0.9992313,0.0003672486,0.0002381821,0.0001161345,0.00001035,0.00003676955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002097973,0.0002637129,0.6345044,0.0001185373,0.000420533,4.860547e-7,0.02526348,0.1819226,0.1484824,0.00007636048,0.0000111656,0.008726566],"study_design_scores_gemma":[0.0009161014,0.00001674733,0.5458463,0.00001701936,0.0001412652,2.87172e-7,0.001758703,0.4332016,0.01767301,0.00007915475,0.0002711166,0.00007866948],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9449393,0.00004224905,0.05438625,0.0002958749,0.00004073825,0.00024512,0.000007155604,0.000005865082,0.00003744028],"genre_scores_gemma":[0.9954984,0.00001004827,0.004190788,0.0001657681,0.00001096254,0.00001519194,0.000009382244,0.000005308658,0.00009419659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.251279,"threshold_uncertainty_score":0.2861592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486517970929118,"score_gpt":0.2799548784879383,"score_spread":0.2650896987786471,"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."}}