{"id":"W2965869110","doi":"","title":"The Use of Story Maps to Understand Air Pollution at School Locations in Calgary.","year":2018,"lang":"en","type":"article","venue":"Florence Research (University of Florence)","topic":"Social Development and Education Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Air pollution; Geography; Environmental planning; Cartography; Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010959,0.0003760418,0.0001420315,0.002728044,0.0008747294,0.002912094,0.0007821885,0.0005260948,0.003655228],"category_scores_gemma":[0.009365189,0.0002627809,0.0001994564,0.002695576,0.0009425532,0.002838152,0.001944262,0.0005707586,0.0003022539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002336576,"about_ca_system_score_gemma":0.001115014,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1399986,"about_ca_topic_score_gemma":0.2157056,"domain_scores_codex":[0.999157,0.0006034295,0.00002718105,0.00006940997,0.00008171415,0.00006140007],"domain_scores_gemma":[0.9954841,0.002986876,0.0006101101,0.0002618887,0.0004270352,0.0002301288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001541943,0.0001188878,0.4850312,0.0006572134,0.0001544333,0.001492559,0.1964179,0.006585096,0.0008477469,0.01482524,0.03472252,0.258993],"study_design_scores_gemma":[0.00002051099,0.0000896886,0.5645855,0.0007278727,0.0001037783,0.0003971445,0.2880879,0.01455118,0.0007436649,0.01197601,0.1186428,0.00007396768],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8975426,0.0037776,0.01857046,0.008123307,0.0001342584,0.0002677783,0.01509672,0.0004262385,0.05606102],"genre_scores_gemma":[0.9815679,0.001079261,0.01336189,0.0001427895,0.00001765197,0.0001178858,0.002365858,0.00003725312,0.001309455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8600013,"threshold_uncertainty_score":0.2783675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1923089190143113,"score_gpt":0.3811413851683753,"score_spread":0.188832466154064,"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."}}