{"id":"W2172495089","doi":"","title":"Probabilistic Linear Discriminant Analysis for Inferences About Identity.","year":2007,"lang":"en","type":"other","venue":"UCL Discovery (University College London)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Facial recognition system; Artificial intelligence; Linear discriminant analysis; Face (sociological concept); Computer science; Identity (music); Pattern recognition (psychology); Probabilistic logic; Three-dimensional face recognition; Noise (video); Variation (astronomy); Generative model; Computer vision; Position (finance); Generative grammar; Machine learning; Face detection; Image (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"],"consensus_categories":[],"category_scores_codex":[0.0002831883,0.0004201905,0.0008425689,0.002985953,0.0003154836,0.0002338045,0.001624299,0.0003199512,0.0007044009],"category_scores_gemma":[0.00006308637,0.0004293703,0.000967635,0.004163756,0.0001789847,0.001468325,0.0004686387,0.0002140856,0.00020548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001777288,"about_ca_system_score_gemma":0.0002599408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008945828,"about_ca_topic_score_gemma":0.01102751,"domain_scores_codex":[0.9975934,0.0001015879,0.0002724407,0.0009634423,0.0005552898,0.0005138521],"domain_scores_gemma":[0.998254,0.000165083,0.0004053692,0.0008132482,0.0001594812,0.0002028262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001416655,0.0009376344,0.001733829,0.0009020796,0.007307185,0.0005470969,0.0006111159,0.0006826357,0.00002126142,0.7660851,0.2160401,0.004990214],"study_design_scores_gemma":[0.003169037,0.0003205863,0.004331235,0.0006417559,0.008904585,0.00001270851,0.001988882,0.1603327,0.00003319255,0.004147388,0.813005,0.003112928],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.000551609,0.0002560141,0.9179811,0.0003093962,0.0003972391,0.00065439,0.00225351,0.0003070946,0.07728959],"genre_scores_gemma":[0.01216432,0.0003746214,0.01349241,0.0001496866,0.0001469635,0.000005804903,0.0003652835,0.00009137864,0.9732096],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9044887,"threshold_uncertainty_score":0.9998158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916790911265012,"score_gpt":0.2461209795798805,"score_spread":0.2269530704672303,"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."}}