{"id":"W2004693117","doi":"10.1016/j.atmosenv.2011.04.007","title":"Application of a tagged-species method to source apportionment of primary PM2.5 components in a regional air quality model","year":2011,"lang":"en","type":"article","venue":"Atmospheric Environment","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Resources Canada","keywords":"Apportionment; Air quality index; Environmental science; Primary (astronomy); Quality (philosophy); Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002090775,0.0006507536,0.0006107998,0.0005486779,0.0005988651,0.0006389987,0.00244878,0.001026754,0.002274573],"category_scores_gemma":[0.004437568,0.0004614668,0.001185689,0.0006869572,0.0002940752,0.0007047514,0.0007568485,0.0007874217,0.0002936555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008183842,"about_ca_system_score_gemma":0.001588717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09340555,"about_ca_topic_score_gemma":0.05265142,"domain_scores_codex":[0.9995053,0.0002642555,0.0000303523,0.0001012007,0.00006107131,0.000037734],"domain_scores_gemma":[0.9983163,0.0009725507,0.0001085217,0.0002010323,0.0003293028,0.00007227989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000072785,0.00004353691,0.003368442,0.00001975648,0.0001273791,0.00005002817,0.00003334603,0.9851569,0.001128534,0.002867413,0.0004180875,0.006713801],"study_design_scores_gemma":[0.00002318134,0.00001833047,0.00042194,0.00000163822,0.00001926021,0.000008592046,0.000007136737,0.9981565,0.0004081852,0.0006365556,0.0002889289,0.000009779549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3114781,0.000188365,0.6789055,0.0003257261,0.000164502,0.0001490204,0.002270408,0.001428604,0.005089771],"genre_scores_gemma":[0.8772702,0.00008181158,0.1183487,0.00009322989,0.00004097484,0.0001587222,0.001250344,0.0003244892,0.002431523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09340555,"threshold_uncertainty_score":0.1857237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.105141032647812,"score_gpt":0.2978931201704491,"score_spread":0.1927520875226371,"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."}}