{"id":"W2910312212","doi":"10.1111/ina.12536","title":"Quantitative filter forensics with residential HVAC filters to assess indoor concentrations","year":2019,"lang":"en","type":"article","venue":"Indoor Air","topic":"Indoor Air Quality and Microbial Exposure","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Housing and Urban Development","keywords":"HVAC; Environmental science; Filter (signal processing); Ventilation (architecture); Contamination; Air conditioning; Environmental engineering; Meteorology; Computer science; Engineering; Ecology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000257213,0.000233354,0.0002558412,0.00005051987,0.0001828399,0.00007409856,0.000342029,0.0001145303,0.00265072],"category_scores_gemma":[0.00005160262,0.0002007924,0.00008365464,0.0003585299,0.0002035874,0.0004789446,0.0001727777,0.0002505343,0.003952241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001728145,"about_ca_system_score_gemma":0.00006263651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000662206,"about_ca_topic_score_gemma":0.001759886,"domain_scores_codex":[0.9981766,0.0001275802,0.0003089047,0.0004858845,0.0004188369,0.0004821729],"domain_scores_gemma":[0.9991345,0.0001283357,0.0001185096,0.0004045313,0.00004053927,0.0001735784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002619776,0.000524446,0.254301,0.0001155053,0.0003225939,0.0001685474,0.01850953,0.01681261,0.4634882,0.01890161,0.220202,0.004034135],"study_design_scores_gemma":[0.009097915,0.00565393,0.3776172,0.0004377213,0.0002098896,0.0001448564,0.007302836,0.001065473,0.3916527,0.002352872,0.2011778,0.003286849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781473,0.000005799758,0.004385945,0.001554194,0.0004401419,0.0008842901,0.00009664608,0.00007313483,0.01441259],"genre_scores_gemma":[0.98636,0.000001392631,0.006856581,0.002625046,0.0000609975,0.00003591835,0.00004690726,0.00003036888,0.003982802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1233161,"threshold_uncertainty_score":0.998261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02371941031032551,"score_gpt":0.2638065480910312,"score_spread":0.2400871377807057,"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."}}