{"id":"W2052453177","doi":"10.1016/j.visres.2013.07.015","title":"Implicit learning of geometric eigenfaces","year":2013,"lang":"en","type":"article","venue":"Vision Research","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Canadian Institutes of Health Research; Canadian Institute for Advanced Research","keywords":"Eigenface; Encoding (memory); Computer science; Artificial intelligence; Pattern recognition (psychology); Principal (computer security); Feature (linguistics); Machine learning; Cognitive psychology; Computer vision; Facial recognition system; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007193564,0.0000544329,0.00008894694,0.0007750043,0.0001762848,0.00007274466,0.0001919588,0.00005829054,0.01057453],"category_scores_gemma":[0.001647581,0.00004424829,0.00003821249,0.001729266,0.0001237688,0.0001957685,0.0001142125,0.0003513311,0.006648885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000217295,"about_ca_system_score_gemma":0.0000277103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002436068,"about_ca_topic_score_gemma":0.000001575998,"domain_scores_codex":[0.9981909,0.0003989245,0.0001571407,0.000239292,0.0007181361,0.0002955492],"domain_scores_gemma":[0.9988538,0.0006473623,0.00003414097,0.0001474486,0.0002204986,0.00009677625],"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":[0.000005744566,0.00004189921,0.0008849911,0.00001502613,4.88188e-7,6.279673e-7,0.00008493991,0.00001833689,0.8734007,0.0001351337,0.002304232,0.1231078],"study_design_scores_gemma":[0.0003870167,0.0007095876,0.08715678,0.00004934704,0.000001269147,0.000007598152,0.0007279824,0.005348912,0.881983,0.001191219,0.02228557,0.0001516546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780828,0.00002680707,0.0001399316,0.0004007729,0.00004504506,0.0002460924,0.000002256569,0.00003729826,0.02101897],"genre_scores_gemma":[0.9952772,0.0003348227,0.00006439521,0.00004975496,0.00002341086,0.00002264795,0.000001719677,0.000009054758,0.00421699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1229562,"threshold_uncertainty_score":0.9941245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1666075518686584,"score_gpt":0.450806965457848,"score_spread":0.2841994135891896,"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."}}