{"id":"W2019454785","doi":"10.1049/iet-bmt.2013.0003","title":"Vocabulary harmonisation for biometrics: the development of ISO/IEC 2382 Part 37","year":2013,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Vocabulary; Computer science; Biometrics; Field (mathematics); Commission; European commission; Process (computing); Software engineering; Computer security; Linguistics; European union; Business; Political science; Law; Programming language","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":[0.0009042674,0.000149392,0.0002125113,0.001513896,0.000187974,0.0002026911,0.00116352,0.0001039661,0.00001589959],"category_scores_gemma":[0.0008229735,0.0001005423,0.00009201333,0.008893697,0.00008866963,0.0003408292,0.0002634172,0.00006697336,0.00008665826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006164561,"about_ca_system_score_gemma":0.0001372794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004055389,"about_ca_topic_score_gemma":0.00000392057,"domain_scores_codex":[0.9983359,0.00003781917,0.0004899373,0.0003264585,0.0004724346,0.0003374788],"domain_scores_gemma":[0.9979684,0.0007896851,0.0002480287,0.0005833803,0.0003430809,0.00006747344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005262619,0.0002097497,0.006356883,0.00009656644,0.00009083372,0.000001186991,0.001579604,0.000007245199,0.002866358,0.004477139,0.02495799,0.9593512],"study_design_scores_gemma":[0.002066506,0.0007944411,0.2877537,0.00007707181,0.00007455872,0.0000190328,0.001314576,0.04302789,0.2331415,0.01576753,0.4146726,0.001290531],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1020117,0.0015107,0.8920847,0.002015518,0.001224815,0.0006553049,0.000007208611,0.0001324284,0.0003576398],"genre_scores_gemma":[0.6840236,0.0001103068,0.3149456,0.0003597228,0.00009347047,0.0001312474,0.00001483243,0.00001270468,0.0003085728],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9580606,"threshold_uncertainty_score":0.4273126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06489102743368283,"score_gpt":0.2733501421185968,"score_spread":0.208459114684914,"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."}}