{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179785,0.0005619893,0.000236931,0.002070969,0.0005797336,0.0006830755,0.0003954526,0.0005443313,0.001151236],"category_scores_gemma":[0.001157573,0.0001967604,0.0003025754,0.001218354,0.0004849949,0.0004220876,0.0004584239,0.0003120613,0.0003381032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000367382,"about_ca_system_score_gemma":0.0002548737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002654267,"about_ca_topic_score_gemma":0.003852918,"domain_scores_codex":[0.9989929,0.0002449334,0.00004563892,0.000246267,0.0003972553,0.0000730657],"domain_scores_gemma":[0.9991903,0.0001897564,0.0002345508,0.00008070941,0.0002804741,0.00002423255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003224796,0.0003166794,0.1855738,0.0004138854,0.0001949843,0.0003926481,0.001624025,0.002523878,0.7039083,0.0009652352,0.0005544233,0.1032095],"study_design_scores_gemma":[0.00001411476,0.001031721,0.3102332,0.0001373317,0.0001403369,0.002233711,0.002180726,0.01337772,0.6626012,0.001209289,0.006769216,0.00007139013],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.912442,0.001395285,0.08008951,0.00008636239,0.00003618881,0.0001851975,0.001147057,0.000189402,0.004428861],"genre_scores_gemma":[0.9446679,0.000766716,0.05159684,0.00009957888,0.00002414787,0.0001098336,0.0005094334,0.00003489186,0.002190651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002654267,"threshold_uncertainty_score":0.006239355,"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."}}