{"id":"W4394057355","doi":"10.5281/zenodo.4777367","title":"Data for: Air inequality: global divergence in urban fine particulate matter concentration trends","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Particulates; Divergence (linguistics); Environmental science; Inequality; Geography; Atmospheric sciences; Mathematics; Ecology; Biology; Geology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002010048,0.0002399865,0.0003114216,0.00001591573,0.0001002925,0.00004963218,0.0007451257,0.0002666043,0.4302801],"category_scores_gemma":[0.0006731883,0.0002392779,0.00004971926,0.0003360138,0.00001994035,0.0003572237,0.0009817369,0.0002240465,0.005393149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003194548,"about_ca_system_score_gemma":0.00008376965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000612972,"about_ca_topic_score_gemma":0.008609205,"domain_scores_codex":[0.997867,0.0001431072,0.0004702197,0.0006578647,0.0003577978,0.0005039331],"domain_scores_gemma":[0.998314,0.00009041273,0.0002347949,0.001145007,0.00001252963,0.0002033156],"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":[0.00001326198,0.00006378625,0.002594801,0.000324627,0.000006429063,0.00001917724,0.0000205992,0.00008529118,1.857101e-7,3.892688e-7,0.9960779,0.0007935756],"study_design_scores_gemma":[0.0002422854,0.0000288706,0.06476764,0.0005460146,0.00001545169,0.000002616393,0.000005359894,0.0004502859,0.000003026256,0.00001589331,0.9336674,0.0002551743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004676629,0.0001249153,0.000002361347,0.001391802,0.0001183156,0.000259917,0.9979426,0.00001546726,0.00009790843],"genre_scores_gemma":[0.0002364696,0.000009696832,0.00006105757,0.004038402,0.0001403186,0.0001059614,0.9952832,0.000008407513,0.0001165201],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4248869,"threshold_uncertainty_score":0.9953813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.142324320297496,"score_gpt":0.3677946342741383,"score_spread":0.2254703139766423,"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."}}