{"id":"W6905006345","doi":"10.15139/s3/tcr6bb","title":"2017 v1 NEI Emissions Modeling Platform (Premerged CMAQ-ready Emissions)","year":2020,"lang":"en","type":"dataset","venue":"UNC Dataverse","topic":"","field":"","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CMAQ; Greenhouse gas; Emission inventory; Pollutant; Air pollution; Smoke; Methane emissions","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0008444468,0.001764798,0.001645667,0.0008484973,0.001065966,0.0003391236,0.004200213,0.001407756,0.01969721],"category_scores_gemma":[0.003356178,0.001673511,0.0005055062,0.001033642,0.0002036955,0.001384768,0.003253412,0.003268027,0.3880638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006546539,"about_ca_system_score_gemma":0.001463913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002289885,"about_ca_topic_score_gemma":0.0004729308,"domain_scores_codex":[0.9918813,0.0002015309,0.001685185,0.002461378,0.00209642,0.001674214],"domain_scores_gemma":[0.9902304,0.0002307085,0.001034143,0.006010473,0.0003355721,0.002158695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003542035,0.0003243342,0.00000195002,0.0002590081,0.0003691853,0.000587335,0.0001067996,0.001353386,0.001049996,0.00004042086,0.9954187,0.0001346522],"study_design_scores_gemma":[0.001631735,0.00007985063,9.577083e-7,0.0007363892,0.001050716,0.00006395174,0.0004513399,0.03005711,0.00005303378,0.0001296033,0.9639505,0.001794782],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003764694,0.00006039653,0.0001457931,0.000061514,0.001631035,0.001098423,0.9949999,0.0006966597,0.001268596],"genre_scores_gemma":[0.00003069327,0.0006366367,0.001399223,0.0005450066,0.001109602,0.00007802004,0.994602,0.0004016343,0.001197137],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3683665,"threshold_uncertainty_score":0.9998886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0877747145527548,"score_gpt":0.3150431744506523,"score_spread":0.2272684598978975,"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."}}