{"id":"W2161044029","doi":"10.1039/b615795e","title":"A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA","year":2007,"lang":"en","type":"article","venue":"Journal of Environmental Monitoring","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; Health Canada; Southern California Environmental Health Sciences Center","keywords":"Context (archaeology); Linear regression; Environmental science; Land use; Metropolitan area; Particulates; Variance (accounting); Regression analysis; Air pollution; Geography; Regression; Physical geography; Statistics; Mathematics; Engineering; Ecology; Archaeology; Civil engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001013956,0.0001644887,0.0002151442,0.00002933528,0.0002808769,0.00004368216,0.0001418249,0.00009493398,0.0001418108],"category_scores_gemma":[0.00004537165,0.0001319863,0.0001071848,0.00005103264,0.00008819481,0.0006454562,0.0001360179,0.0002535396,0.00002631971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00052083,"about_ca_system_score_gemma":0.000006617824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004495819,"about_ca_topic_score_gemma":0.00003906158,"domain_scores_codex":[0.9981497,0.00003153244,0.0006132241,0.0001807468,0.0004732205,0.0005515935],"domain_scores_gemma":[0.9989403,0.0001205131,0.0004358391,0.0001531146,0.000004328565,0.0003458812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002353616,0.0001527273,0.9566612,0.00002354179,0.00001311568,0.00002483344,0.0009219808,0.01388043,0.0232252,2.568271e-7,0.001184793,0.003676621],"study_design_scores_gemma":[0.000906309,0.0001917719,0.9709014,0.000183395,0.00002838026,0.00006556493,0.000264,0.01098435,0.01485715,0.00004858753,0.001397235,0.0001718697],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934525,0.00008566862,0.00569808,0.0001528825,0.000323245,0.0001894135,0.00006691764,0.000007386203,0.0000238554],"genre_scores_gemma":[0.991308,0.00002990146,0.007775962,0.0001652595,0.0003670969,0.000003509112,0.000002694903,0.00002358088,0.0003239436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01424025,"threshold_uncertainty_score":0.5382246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05372259977377697,"score_gpt":0.3338113051149715,"score_spread":0.2800887053411946,"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."}}