{"id":"W2460787262","doi":"10.1039/c6en00122j","title":"Evaluation of existing control measures in reducing health and safety risks of engineered nanomaterials","year":2016,"lang":"en","type":"article","venue":"Environmental Science Nano","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Reach Technologies (Canada); Institute of Particle Physics","funders":"","keywords":"Risk analysis (engineering); Risk management; Control (management); Health risk; Nanomaterials; Risk assessment; Business; Computer science; Environmental health; Nanotechnology; Computer security; Materials science; Medicine; Artificial intelligence","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":[0.005884392,0.00007011576,0.000177821,0.00006848895,0.0001014381,0.00001196549,0.0001533835,0.00001967911,0.0001340059],"category_scores_gemma":[0.0003087865,0.00004944632,0.00001534136,0.0001296654,0.0004789851,0.000200538,0.00004643656,0.00001184937,0.000006698479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002007434,"about_ca_system_score_gemma":0.0001155452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009150854,"about_ca_topic_score_gemma":0.000004141207,"domain_scores_codex":[0.9983324,0.0001611418,0.0004062734,0.0002515334,0.0006426467,0.0002059386],"domain_scores_gemma":[0.9994034,0.00009951126,0.0002242093,0.0001790172,0.00001757943,0.00007628293],"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":[0.0000115243,0.00004420765,0.00169723,0.000004207101,8.617914e-7,3.349824e-8,0.0002431535,0.0001277535,0.9659893,0.0001480409,8.18537e-7,0.03173285],"study_design_scores_gemma":[0.0005486057,0.00004595738,0.03917424,0.00007759126,0.000007173379,0.000001357423,0.0001294278,0.0002988739,0.9594992,0.0001393991,0.00002054319,0.00005761298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988755,0.0002660069,0.0002636244,0.000112478,0.00004296574,0.0003067085,0.00004583125,0.000006473927,0.00008042619],"genre_scores_gemma":[0.999103,0.00005715371,0.0007869601,0.000007721998,0.00001032729,0.00002562749,3.66462e-7,0.000004121679,0.000004696969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03747701,"threshold_uncertainty_score":0.2039424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08385927667329517,"score_gpt":0.3278549872855273,"score_spread":0.2439957106122321,"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."}}