{"id":"W2982068843","doi":"10.1088/1757-899x/609/3/032032","title":"Development of a procedure to measure the performance of ventilation filters for nanoparticles","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Aerosol Filtration and Electrostatic Precipitation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"","keywords":"Filtration (mathematics); Nanoparticle; Ventilation (architecture); Materials science; Measure (data warehouse); Filter (signal processing); Process engineering; Particle size; Particle (ecology); Computer science; Nanotechnology; Biological system; Chemical engineering; Mathematics; Engineering; Mechanical engineering; Statistics; Data mining; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00338365,0.001258298,0.000720425,0.001673365,0.0007375273,0.0006883315,0.0009520122,0.001618259,0.001533757],"category_scores_gemma":[0.004351816,0.000438709,0.0007874903,0.0007965667,0.0006207749,0.000808466,0.001000196,0.001240179,0.001484446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006122266,"about_ca_system_score_gemma":0.001372163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001260462,"about_ca_topic_score_gemma":0.001419204,"domain_scores_codex":[0.9962227,0.0005368647,0.0002554806,0.0006215881,0.002150197,0.00021306],"domain_scores_gemma":[0.9967722,0.00100799,0.0005015489,0.000400962,0.001229626,0.00008764077],"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":[0.00005134744,0.0001070016,0.0008818661,0.0001575685,0.00002932384,0.00003312651,0.00009151008,0.0002723121,0.9884189,0.0002220183,0.0002129849,0.009521961],"study_design_scores_gemma":[0.000006854694,0.0005959285,0.003661191,0.00001788905,0.00003468045,0.00009734149,0.00004101501,0.001846517,0.9904014,0.00009248253,0.003174482,0.00003011648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3106251,0.002950841,0.6748245,0.0003003652,0.0004037756,0.002222802,0.002596606,0.002502937,0.003573019],"genre_scores_gemma":[0.4455441,0.002471771,0.5382621,0.0003214666,0.000109307,0.004562153,0.0031939,0.0005013401,0.005033887],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00338365,"threshold_uncertainty_score":0.01789469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446885131988586,"score_gpt":0.2058361544528425,"score_spread":0.1913673031329566,"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."}}