{"id":"W3120308696","doi":"10.1007/s10708-020-10345-7","title":"Spatial regression modelling of particulate pollution in Calgary, Canada","year":2021,"lang":"en","type":"article","venue":"GeoJournal","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Health Canada; University of Calgary","keywords":"Particulates; Environmental science; Atmospheric sciences; Air pollution; Pollution; Spatial ecology; Regression analysis; Physical geography; Pollutant; Spatial variability; Statistics; Geography; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001153395,0.0007774571,0.000982087,0.001453415,0.001386321,0.002228996,0.002424349,0.0009807714,0.003493323],"category_scores_gemma":[0.003541931,0.000672457,0.001038442,0.003051616,0.0007322723,0.0004900255,0.0008965288,0.00104124,0.0004882488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02226584,"about_ca_system_score_gemma":0.02444475,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.996968,"about_ca_topic_score_gemma":0.9951159,"domain_scores_codex":[0.999464,0.00009947159,0.00003375814,0.0001237688,0.00009487989,0.0001842703],"domain_scores_gemma":[0.998305,0.0005322566,0.0001188242,0.00007044667,0.0008661045,0.0001074595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002001883,0.0001019444,0.1026068,0.00008032456,0.0002701485,0.0002017715,0.0001792506,0.8736897,0.0004421105,0.002660223,0.004847158,0.01472035],"study_design_scores_gemma":[0.00003911472,0.00002205861,0.06594436,0.00004056878,0.00007576949,0.0000188864,0.00057552,0.9300354,0.0002947637,0.0005363863,0.002372073,0.00004508238],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983557,0.001039458,0.004729901,0.000869976,0.00007538904,0.00005020145,0.00536616,0.0002314838,0.004080399],"genre_scores_gemma":[0.9861459,0.0006301316,0.00246346,0.00005990701,0.00001226307,0.00003150402,0.002821445,0.00006527107,0.007770112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02226584,"threshold_uncertainty_score":0.1615507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04693992320184338,"score_gpt":0.2735888466329072,"score_spread":0.2266489234310639,"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."}}