{"id":"W2056165916","doi":"10.4018/jnmc.2010070103","title":"Nano-Based Food and Substantial Equivalence","year":2010,"lang":"en","type":"article","venue":"International Journal of Nanotechnology and Molecular Computation","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Mistake; Equivalence (formal languages); Risk analysis (engineering); Computer science; Key (lock); Engineering; Mathematics; Business; Political science; Computer security; Law","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":[0.0001641859,0.00005917334,0.00007766376,0.00005538026,0.00004509209,0.00004343076,0.000196985,0.0001257481,0.00001905883],"category_scores_gemma":[0.0001226666,0.00002941009,0.00002842455,0.00008055824,0.000140712,0.00005057816,0.00006108693,0.0002196056,0.000001148098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006157015,"about_ca_system_score_gemma":0.00001718429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004557513,"about_ca_topic_score_gemma":0.00002676129,"domain_scores_codex":[0.999375,0.00002814359,0.0001619509,0.0001073121,0.0002357766,0.00009179224],"domain_scores_gemma":[0.999478,0.00008077853,0.00009989591,0.0000185981,0.0002768511,0.00004591048],"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":[0.00003245016,0.00002394907,0.0004018203,0.000001094764,0.00001571926,0.00002591231,0.000009076709,0.00002306791,0.8923192,0.002327789,0.00000458258,0.1048154],"study_design_scores_gemma":[0.0007801452,0.001173217,0.02501553,0.00002380551,0.00001604037,0.0005813431,0.00005408908,0.001851934,0.9501417,0.01911514,0.001096684,0.0001503552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841488,0.0001237806,0.01003951,0.00540386,0.0002053282,0.00004568323,0.000003865538,0.0000122291,0.00001696286],"genre_scores_gemma":[0.9961818,0.00002731593,0.003597259,0.0001382656,0.00004906429,7.329779e-7,0.000003389289,7.688419e-7,0.000001435969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.104665,"threshold_uncertainty_score":0.1199309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272462070378786,"score_gpt":0.2554245974866317,"score_spread":0.2426999767828438,"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."}}