{"id":"W4403447396","doi":"10.1109/idap64064.2024.10710650","title":"AI_r: Transforming Air Quality Monitoring through Cost-Effective AI Solutions","year":2024,"lang":"en","type":"article","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Computer science; Quality (philosophy); Air quality index; Reliability engineering; Engineering; Meteorology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.00252089,0.0014372,0.0005480978,0.001279629,0.0004922396,0.002686768,0.003179493,0.001684756,0.01115401],"category_scores_gemma":[0.005082653,0.0003990272,0.000775172,0.001247879,0.0007287296,0.004091769,0.002993945,0.002267253,0.005805655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009149999,"about_ca_system_score_gemma":0.001373085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003502858,"about_ca_topic_score_gemma":0.002227317,"domain_scores_codex":[0.9981148,0.0004582513,0.00007715981,0.0003107148,0.0008447912,0.0001943786],"domain_scores_gemma":[0.99734,0.0007053551,0.0002243,0.0005535113,0.0009614819,0.0002152901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003835889,0.0006358171,0.002648884,0.0008183089,0.0001528274,0.0002111705,0.0001899769,0.06039323,0.03204461,0.05303936,0.09403035,0.7554518],"study_design_scores_gemma":[0.0002486717,0.000730993,0.003112064,0.0002681839,0.0001126003,0.0002930269,0.0003942795,0.5478954,0.0298599,0.08572005,0.3312348,0.0001300262],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02016979,0.002289337,0.854442,0.01042279,0.001224602,0.000582799,0.001830464,0.04485678,0.0641814],"genre_scores_gemma":[0.3358448,0.004166183,0.6279451,0.00318357,0.0007349217,0.0006373439,0.00434534,0.002270542,0.02087206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01115401,"threshold_uncertainty_score":0.03731394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.071627678857198,"score_gpt":0.3569329523894755,"score_spread":0.2853052735322775,"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."}}