{"id":"W2914146692","doi":"10.22111/ijfs.2019.4778","title":"A comprehensive experimental comparison of the aggregation techniques for face recognition","year":2019,"lang":"en","type":"article","venue":"Iranian journal of fuzzy systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Salient; Pattern recognition (psychology); Artificial intelligence; Redundancy (engineering); Facial recognition system; Face (sociological concept); Similarity (geometry); Data mining; Machine learning; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002504268,0.00009729753,0.000282195,0.0001019893,0.00006488405,0.00006961708,0.0004332607,0.00006788009,0.000004091937],"category_scores_gemma":[0.00001909223,0.00006623639,0.0001572985,0.0001500237,0.0000239528,0.0004388604,0.00004194907,0.0001140259,0.00001270866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004134447,"about_ca_system_score_gemma":0.00004253328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008473733,"about_ca_topic_score_gemma":3.91386e-7,"domain_scores_codex":[0.9987428,0.0001415894,0.0005534479,0.0001191524,0.0003273745,0.000115595],"domain_scores_gemma":[0.9983628,0.0001043148,0.0008545441,0.0002049026,0.0004293426,0.00004406646],"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":[0.0002512028,0.0004219012,0.003424491,0.0005360638,0.0001148411,0.000003164783,0.009048269,0.0004329825,0.9057415,0.0006479159,0.007002782,0.07237488],"study_design_scores_gemma":[0.001686134,0.001346689,0.001015092,0.002528842,0.00002631823,0.000181721,0.006379519,0.009536736,0.9695637,0.0007710381,0.006712255,0.000251912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418069,0.001167419,0.05318418,0.0002416996,0.002008701,0.000935213,0.00001352327,0.00002937195,0.0006130309],"genre_scores_gemma":[0.9946985,0.00000676813,0.005093783,0.00004335126,0.00009546553,0.00001202439,0.000001693461,0.000007078748,0.0000412845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07212297,"threshold_uncertainty_score":0.2701041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04185824577761649,"score_gpt":0.2982797176718495,"score_spread":0.256421471894233,"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."}}