{"id":"W2209501224","doi":"10.1002/9780470522356.ch23","title":"Measuring Information Content in Biometric Features","year":2009,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Biometrics; Content (measure theory); Computer science; Artificial intelligence; Information retrieval; Mathematics","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":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0004136582,0.0002618096,0.0003688772,0.02229989,0.00005377765,0.0003569451,0.0006865888,0.000328735,0.00004301519],"category_scores_gemma":[0.0002698751,0.0002477381,0.0002130399,0.00646745,0.00002378182,0.0005306051,0.0001438333,0.0002849517,0.0005458978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002630335,"about_ca_system_score_gemma":0.0000662881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003813493,"about_ca_topic_score_gemma":0.000007322736,"domain_scores_codex":[0.9981735,0.00001518921,0.0004961449,0.0002939869,0.0007671763,0.0002540362],"domain_scores_gemma":[0.9987807,0.00009456278,0.0003103459,0.0004019909,0.0002905689,0.0001218],"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":[0.000001543589,0.00002026687,0.00001801756,0.00002592522,0.00002921643,0.00001116927,0.00003131402,0.000004196342,0.0000136958,0.01924029,0.001527797,0.9790766],"study_design_scores_gemma":[0.0009841045,0.0001842671,0.004687133,0.000294137,0.00006310421,0.00003174512,0.000018293,0.001565458,0.000440756,0.006109428,0.9844331,0.001188482],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00003444759,0.007207433,0.1064802,0.0009831028,0.0008684991,0.0004715278,0.00005894606,0.0003658997,0.88353],"genre_scores_gemma":[0.06013841,0.01721061,0.05655192,0.00638427,0.0005435447,0.00003901338,0.0007771066,0.0001124366,0.8582427],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9829053,"threshold_uncertainty_score":0.9999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09694093503159364,"score_gpt":0.231914277427622,"score_spread":0.1349733423960283,"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."}}