{"id":"W2160405573","doi":"10.1038/jes.2012.26","title":"A spatiotemporal land-use regression model of winter fine particulate levels in residential neighbourhoods","year":2012,"lang":"en","type":"article","venue":"Journal of Exposure Science & Environmental Epidemiology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de Santé Publique du Québec; GDG Environnement; Université de Montréal; Sante Montreal","funders":"Health Canada","keywords":"Particulates; Environmental science; Regression analysis; Geography; Land use; Geographically Weighted Regression; Meteorology; Physical geography; Atmospheric sciences; Environmental engineering; Statistics; Engineering; Civil engineering; Geology; Mathematics; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007809512,0.0001895618,0.0005907145,0.0001569092,0.0001329921,0.000009327059,0.000435523,0.0001564878,0.0006888667],"category_scores_gemma":[0.001260194,0.0001380422,0.0001336902,0.0002217507,0.001286491,0.001820132,0.0002981715,0.0004064276,0.00003715479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00043031,"about_ca_system_score_gemma":0.00006528629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001801784,"about_ca_topic_score_gemma":0.0000638186,"domain_scores_codex":[0.9963703,0.0005658732,0.001467164,0.0002598695,0.0005453444,0.0007914385],"domain_scores_gemma":[0.9976987,0.00034563,0.001161531,0.0002857763,0.00000785126,0.0005005698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001291345,0.0002392022,0.9486133,0.00000693432,0.000004386308,0.000005180578,0.0009614528,0.0232925,0.02448521,0.0000743112,0.0004355292,0.001752844],"study_design_scores_gemma":[0.0006206062,0.0004212964,0.9877759,0.0000615855,0.00001364713,0.00007463104,0.00007823329,0.004802272,0.004534842,0.001304541,0.0001718129,0.000140581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956633,0.0001913387,0.001918919,0.001714363,0.0002770837,0.0001279646,0.00001175362,0.000003969443,0.00009128858],"genre_scores_gemma":[0.990767,0.00006960259,0.008110624,0.0008835969,0.00009474727,0.000001575753,0.000001633119,0.00001058079,0.00006062392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03916264,"threshold_uncertainty_score":0.7542607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1195076492404731,"score_gpt":0.3637602853192566,"score_spread":0.2442526360787835,"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."}}