{"id":"W4410298533","doi":"10.3390/atmos16050574","title":"Indoor Air Quality Assessment Through IoT Sensor Technology: A Montreal–Qatar Case Study","year":2025,"lang":"en","type":"article","venue":"Atmosphere","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Concordia University","funders":"Qatar University; Concordia University","keywords":"Air quality index; Environmental science; Internet of Things; Remote sensing; Indoor air quality; Meteorology; Quality (philosophy); Computer science; Geography; Environmental engineering; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565501,0.0002311861,0.0003022551,0.000004578651,0.0004447888,0.00003081623,0.0002769307,0.000153342,0.0002970282],"category_scores_gemma":[0.00009852905,0.0002091834,0.00007220471,0.0006434176,0.0001947228,0.0001189316,0.0004700518,0.0003665223,0.0001231631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003400615,"about_ca_system_score_gemma":0.00003212069,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02780521,"about_ca_topic_score_gemma":0.002678724,"domain_scores_codex":[0.998116,0.0001955612,0.0004232118,0.0005694093,0.0003010711,0.0003947983],"domain_scores_gemma":[0.9989661,0.0001434634,0.0001336437,0.0006756391,0.00001729601,0.00006386767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001651119,0.0006370716,0.9406426,0.00002218385,0.00008041432,0.0009396303,0.001829077,0.001765175,0.0001493796,0.000140504,0.001261285,0.05251615],"study_design_scores_gemma":[0.003743153,0.0008345303,0.785719,0.0001539304,0.0002411819,0.0004622522,0.1792359,0.004193694,0.001307219,0.00507189,0.01778888,0.001248324],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831962,0.00006111115,0.001403481,0.0007981818,0.000247124,0.0005013038,0.000007605144,0.0002858659,0.01349911],"genre_scores_gemma":[0.9802938,0.000003012849,0.0165103,0.0001681852,0.00004661886,0.00006999086,0.000001697798,0.00001643997,0.002890025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1774068,"threshold_uncertainty_score":0.9786687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03046822967173378,"score_gpt":0.3381918743945173,"score_spread":0.3077236447227835,"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."}}