{"id":"W6930050212","doi":"10.5281/zenodo.10951322","title":"Supporting data for \"Health benefits of US light-duty vehicle electrification: roles of fleet dynamics, clean electricity, and policy timing\"","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hormonal and reproductive studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Government (linguistics); Key (lock); Data collection; Work (physics); Public policy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001644438,0.001293932,0.001060351,0.001941996,0.0004183811,0.00176343,0.001857733,0.001911567,0.255318],"category_scores_gemma":[0.01347378,0.0007258159,0.001683385,0.00444403,0.0002806719,0.001345962,0.001587137,0.001413707,0.07626442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001865205,"about_ca_system_score_gemma":0.002880079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03555316,"about_ca_topic_score_gemma":0.05033172,"domain_scores_codex":[0.9990247,0.0002317991,0.0001629918,0.0002072478,0.0002449218,0.0001281828],"domain_scores_gemma":[0.9942034,0.003049572,0.0007113409,0.0006182456,0.001097044,0.0003204345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006356128,0.00001649248,0.001421708,0.00100736,0.00005347774,0.00001556681,0.00001485297,0.0008240395,0.00003135426,0.0007338997,0.9942294,0.001588357],"study_design_scores_gemma":[0.001434732,0.00003593571,0.0108211,0.002038339,0.0001165037,0.00007814993,0.000149079,0.001582816,0.000251648,0.004037776,0.9794014,0.00005255948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004519432,0.00002258313,0.00002814595,0.00006109452,0.00001262699,0.000007000879,0.9994113,0.00004886047,0.0003632692],"genre_scores_gemma":[0.00153918,0.0001100172,0.0004012565,0.0001785617,0.00002334219,0.00015946,0.9958734,0.0001198293,0.00159498],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.255318,"threshold_uncertainty_score":0.8541238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05175447979187536,"score_gpt":0.3375854330690057,"score_spread":0.2858309532771303,"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."}}