{"id":"W3033644178","doi":"10.1007/s41810-020-00064-4","title":"Comparative Performance of the NanoScan and the Classic SMPS in Determining N95 Filtering Facepiece Efficiency Against Nanoparticles","year":2020,"lang":"en","type":"article","venue":"Aerosol Science and Engineering","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Scanning mobility particle sizer; Penetration (warfare); Nanoparticle; Materials science; Particle size; Nanotechnology; Environmental science; Biomedical engineering; Composite material; Process engineering; Mathematics; Chemical engineering; Particle-size distribution; Engineering; Operations research","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":[0.001035626,0.0003693934,0.000297777,0.0004739994,0.0003608832,0.0003779518,0.0005580215,0.0007590898,0.001595151],"category_scores_gemma":[0.001754258,0.0002480952,0.0003628378,0.0004039046,0.0002692911,0.0003781679,0.0002552463,0.0002520468,0.0005127977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004568382,"about_ca_system_score_gemma":0.0002930905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00537566,"about_ca_topic_score_gemma":0.01012114,"domain_scores_codex":[0.9989742,0.0001532927,0.00007743547,0.0002188877,0.000465836,0.0001103991],"domain_scores_gemma":[0.998654,0.0006738674,0.0001284531,0.0001125741,0.0003990065,0.0000320566],"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":[0.001108281,0.0001072188,0.004070508,0.0001605841,0.00004278911,0.00007068017,0.000175716,0.002494353,0.9729906,0.0001838182,0.0001970135,0.01839852],"study_design_scores_gemma":[0.000002067371,0.0003295396,0.001860685,0.000003642397,0.00001474054,0.00002723323,0.00003156264,0.002090136,0.9952834,0.000008984001,0.0003405734,0.000007454764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990941,0.001176747,0.005212047,0.00004037012,0.00002324553,0.00002812012,0.0002692354,0.00009245917,0.002216657],"genre_scores_gemma":[0.9868772,0.0006795876,0.008251346,0.0000358886,0.000005916153,0.00002246402,0.0002599941,0.00002239245,0.003845251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00537566,"threshold_uncertainty_score":0.01068872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03189309780253666,"score_gpt":0.2475740964992384,"score_spread":0.2156809986967017,"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."}}