{"id":"W6967020015","doi":"10.48448/pwsk-2s59","title":"Is there an Own-Age Advantage in Talker Recognition?","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Task (project management); Identification (biology); Word identification; Face (sociological concept); Test (biology); Speech perception","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001522259,0.0003604524,0.0003926932,0.0007137634,0.0002760344,0.0009961535,0.0004152982,0.0005030847,0.01396196],"category_scores_gemma":[0.003765632,0.0002532892,0.0002753539,0.0001171393,0.0005236397,0.001731877,0.0009071196,0.0004123301,0.003479345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117473,"about_ca_system_score_gemma":0.0001771366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000747973,"about_ca_topic_score_gemma":0.001733616,"domain_scores_codex":[0.9994355,0.00005703105,0.00005503133,0.0002004057,0.0001629192,0.00008920259],"domain_scores_gemma":[0.9965982,0.001190442,0.000677601,0.0005529223,0.0004231883,0.0005576699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001306689,0.0003752511,0.3546326,0.0004188027,0.0002033891,0.001606218,0.005781942,0.0001213125,0.4184028,0.002103143,0.002443652,0.2126042],"study_design_scores_gemma":[0.00002091299,0.001103766,0.9486436,0.00008407168,0.0001176411,0.005047171,0.002096509,0.0003403338,0.03545688,0.001086157,0.005956216,0.00004675025],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877349,0.001064501,0.001160282,0.0002614438,0.00008794449,0.000009064943,0.000288261,0.00007566322,0.009317953],"genre_scores_gemma":[0.9929795,0.0004685041,0.001770174,0.0001985155,0.00004847658,0.00001423644,0.0003055999,0.00005494882,0.00416004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01396196,"threshold_uncertainty_score":0.04670745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04341269300744299,"score_gpt":0.3349856152913649,"score_spread":0.291572922283922,"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."}}