{"id":"W4412491853","doi":"10.1029/2025gh001378","title":"Incorporating Community Knowledge Into Analysis of Air Quality Monitoring Network Data","year":2025,"lang":"en","type":"article","venue":"GeoHealth","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Strathcona Community Hospital; University of British Columbia","funders":"Canada Research Chairs","keywords":"Air quality index; Air pollution; Computer science; Data science; Environmental resource management; Geography; Environmental science; Meteorology","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":[0.03098307,0.0008778893,0.0008293706,0.006527782,0.0009870243,0.003105375,0.001660692,0.0008627003,0.001473331],"category_scores_gemma":[0.1004631,0.0005784635,0.001193412,0.003719835,0.0008787129,0.003681582,0.00363218,0.001483339,0.0002854202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002281983,"about_ca_system_score_gemma":0.001787184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03397463,"about_ca_topic_score_gemma":0.0491512,"domain_scores_codex":[0.9729014,0.02127761,0.0009348099,0.002449539,0.001911969,0.0005246818],"domain_scores_gemma":[0.8670589,0.1109673,0.006584457,0.008337477,0.006307006,0.0007447884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006095899,0.00111133,0.5103538,0.0006733217,0.001661534,0.0003187998,0.01033979,0.1458541,0.004270771,0.006926032,0.00226947,0.3156114],"study_design_scores_gemma":[0.00004781747,0.000335175,0.07357983,0.0002247985,0.0001531975,0.00006311762,0.005063174,0.9001436,0.002408771,0.0138292,0.004036207,0.0001151099],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6477827,0.0002884977,0.3389976,0.001219194,0.00005211074,0.001129942,0.002661676,0.001114075,0.006754186],"genre_scores_gemma":[0.843509,0.00005158991,0.1543146,0.00008560521,0.00002468352,0.0003570795,0.001091828,0.00005730711,0.000508431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03397463,"threshold_uncertainty_score":0.163856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1682367263552519,"score_gpt":0.4508497243924158,"score_spread":0.2826129980371639,"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."}}