{"id":"W134152664","doi":"10.1007/978-94-007-1359-8_117","title":"Development and Evaluation of Land-Use Regression Models Using Modeled Air Quality Concentrations","year":2011,"lang":"en","type":"book-chapter","venue":"NATO science for peace and security series. C, Environmental security","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Canada","funders":"","keywords":"Air quality index; Air pollution; Environmental science; Particulates; Regression analysis; Environmental engineering; Computer science; Geography; Meteorology; Machine learning; Ecology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003277779,0.0004917809,0.0006013964,0.000107065,0.001047872,0.00006388949,0.000315836,0.0004259473,0.0003571915],"category_scores_gemma":[0.00007525596,0.0004690771,0.0000950976,0.00008769048,0.00237355,0.002118154,0.0005301293,0.0003863868,0.00000650382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009178304,"about_ca_system_score_gemma":0.0002940839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005410883,"about_ca_topic_score_gemma":0.0009106357,"domain_scores_codex":[0.9957439,0.0001127405,0.000845845,0.001025185,0.001684541,0.0005877633],"domain_scores_gemma":[0.9982226,0.00008130792,0.0006819488,0.0004547386,0.00004995459,0.0005094782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004484372,0.003772495,0.0360564,0.003836497,0.000541885,0.00002620516,0.4043807,0.008375202,0.01613426,0.4243751,0.002566898,0.09544998],"study_design_scores_gemma":[0.00701008,0.001218661,0.01791031,0.001144276,0.0009106861,0.0001227886,0.003753851,0.3348607,0.01450498,0.5314535,0.08216785,0.004942372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871276,0.00107096,0.000433448,0.0001511479,0.0001236757,0.001926201,0.000500752,0.00003507,0.008631157],"genre_scores_gemma":[0.9939276,0.001255675,0.003335177,0.0002499339,0.00003035249,0.00003599308,0.0001285009,0.00003530042,0.00100147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4006268,"threshold_uncertainty_score":0.9997761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.127988511362239,"score_gpt":0.3369918659074078,"score_spread":0.2090033545451688,"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."}}