{"id":"W1966411352","doi":"10.1111/ina.12088","title":"Particle characterization in retail environments: concentrations, sources, and removal mechanisms","year":2013,"lang":"en","type":"article","venue":"Indoor Air","topic":"Infection Control and Ventilation","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Texas at Austin; Pennsylvania State University; Texas Commission on Environmental Quality","keywords":"Environmental science; Ventilation (architecture); Particle (ecology); Particle size; Air filtration; Filtration (mathematics); Indoor air; Environmental engineering; Mass concentration (chemistry); Particle number; Indoor air quality; Ultrafine particle; Environmental chemistry; Chemistry; Materials science; Meteorology; Statistics; Ecology; Geography; Mathematics; Physics; Nanotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008274618,0.00005839765,0.00009389738,0.00003226638,0.00004316921,0.00001585638,0.00001314915,0.0000502888,0.0003878601],"category_scores_gemma":[0.0000249823,0.00005337776,0.00001729302,0.00006355172,0.00002140882,0.0001995213,0.000008684617,0.00006437885,0.0001031242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000353933,"about_ca_system_score_gemma":0.00001158949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003250385,"about_ca_topic_score_gemma":0.00000544434,"domain_scores_codex":[0.999486,0.00002789476,0.0001653498,0.0001168773,0.00009305727,0.0001108062],"domain_scores_gemma":[0.9997982,0.00001136008,0.00004836815,0.00007677781,0.00001488516,0.00005040791],"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":[0.00006934522,0.0001576514,0.3191433,0.00001791811,0.00002014676,0.00001169828,0.00039627,0.00001096925,0.6465148,0.002431934,0.00003074978,0.03119525],"study_design_scores_gemma":[0.001695333,0.00008895456,0.9559784,0.00002563565,0.00001812484,0.0000378006,0.00006766777,0.01183858,0.0242024,0.0002358128,0.00573978,0.00007152517],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850776,0.00003914315,0.01285986,0.001270496,0.00006826715,0.0003846696,0.000002484119,0.00002597575,0.0002715521],"genre_scores_gemma":[0.9978627,0.00002265522,0.0001614168,0.000626753,0.00005384025,0.00004072474,0.00005612159,0.000006989512,0.001168823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6368351,"threshold_uncertainty_score":0.4246796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009557974379326005,"score_gpt":0.212448722990085,"score_spread":0.202890748610759,"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."}}