{"id":"W3086739108","doi":"","title":"Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges","year":2017,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Air quality index; Risk analysis (engineering); Computer science; Business; Meteorology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003144,0.0002442666,0.0002488512,0.00003511171,0.001224491,0.0005235452,0.0003645376,0.0001417095,0.000093106],"category_scores_gemma":[0.0003341232,0.0002288819,0.00003218219,0.00003467406,0.00023392,0.0006847752,0.0002058638,0.0002277586,0.00009194434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003433417,"about_ca_system_score_gemma":0.0002094803,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1343121,"about_ca_topic_score_gemma":0.09099413,"domain_scores_codex":[0.9977788,0.0001098224,0.0004326054,0.000537096,0.0004323931,0.0007092317],"domain_scores_gemma":[0.9984987,0.00005768093,0.0003422779,0.0004593453,0.0000729015,0.0005691283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003629152,0.0001306953,0.4536754,0.000177977,0.00006943117,0.00008767362,0.002315329,0.00720914,0.001769974,0.00667243,0.001250071,0.5266056],"study_design_scores_gemma":[0.0002532518,0.00006150709,0.9603069,0.00009370285,0.000009427063,0.00001922261,0.0004021226,0.004911023,0.0004286067,0.0002290287,0.03288409,0.0004010797],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728924,0.0005837136,0.00001760829,0.005811593,0.000526979,0.0002583214,0.00003150072,0.00004884557,0.01982906],"genre_scores_gemma":[0.9965671,0.0006238834,0.001557745,0.00006980813,0.0003582868,0.00003794357,0.00002538368,0.00001849438,0.0007413166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5262045,"threshold_uncertainty_score":0.9417915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1458030561050052,"score_gpt":0.3145842220987172,"score_spread":0.168781165993712,"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."}}