{"id":"W4393497400","doi":"10.5281/zenodo.6557777","title":"Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Maple; Particulates; Environmental science; Earth science; Remote sensing; Geology; Chemistry; Ecology; Biology","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":[0.00129065,0.0009923134,0.0005822523,0.002244486,0.0004017485,0.000993982,0.001401023,0.0004996946,0.007662162],"category_scores_gemma":[0.002826235,0.0005471762,0.001178356,0.002667774,0.0001218,0.0009230631,0.001029389,0.0005703404,0.005798278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005037017,"about_ca_system_score_gemma":0.001468592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03100036,"about_ca_topic_score_gemma":0.0278631,"domain_scores_codex":[0.999445,0.0000830618,0.00003015224,0.0001324744,0.0002596014,0.00004970339],"domain_scores_gemma":[0.9991304,0.00009742149,0.0001097624,0.0002084399,0.0004176591,0.00003615906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002336037,0.0002566957,0.10279,0.001111384,0.001669614,0.0002580572,0.0003513228,0.05751844,0.02060073,0.008754763,0.3680649,0.4383905],"study_design_scores_gemma":[0.0002556568,0.00007765833,0.1616893,0.000176488,0.00044521,0.0002177721,0.0002083955,0.1916494,0.01924652,0.00744209,0.6183845,0.0002070456],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.08629948,0.001121502,0.464256,0.0005749679,0.0004518453,0.000932171,0.3636855,0.04064972,0.04202872],"genre_scores_gemma":[0.1196985,0.0005151519,0.6060832,0.0001818548,0.0001108022,0.001768837,0.2498459,0.004437069,0.01735874],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03100036,"threshold_uncertainty_score":0.06163979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02356752846561724,"score_gpt":0.2386925865403554,"score_spread":0.2151250580747381,"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."}}