{"id":"W2106095926","doi":"10.1027/1864-1105/a000156","title":"How Realistic Should Avatars Be?","year":2015,"lang":"en","type":"article","venue":"Journal of Media Psychology Theories Methods and Applications","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychology; Face (sociological concept); Realism; Functional magnetic resonance imaging; Character (mathematics); Variation (astronomy); Blood-oxygen-level dependent; Cognitive psychology; Core (optical fiber); Social psychology; Computer science; Neuroscience; Sociology; Art","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":[0.001140326,0.0003555744,0.0001303177,0.0001728319,0.0004720209,0.002724935,0.0002221087,0.0006803456,0.006327555],"category_scores_gemma":[0.008810309,0.00009956725,0.0001520845,0.00007395935,0.0007340313,0.001358999,0.0005728427,0.0005889772,0.001384495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003296181,"about_ca_system_score_gemma":0.0001294286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003950676,"about_ca_topic_score_gemma":0.0005319993,"domain_scores_codex":[0.9990049,0.0006699985,0.00002856753,0.00009485603,0.000150749,0.00005091086],"domain_scores_gemma":[0.9979631,0.0007278002,0.0004481799,0.0001142709,0.0004105132,0.0003361293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00202618,0.0005466772,0.2647695,0.001462059,0.0003700414,0.00328026,0.06603507,0.004892554,0.07314332,0.1090555,0.06266373,0.4117552],"study_design_scores_gemma":[0.0002003485,0.001818307,0.1690552,0.001752416,0.0005340132,0.01549649,0.1305643,0.03058694,0.03196596,0.1132036,0.504405,0.0004173928],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8476344,0.001854623,0.02320648,0.007484599,0.0009391673,0.00007413617,0.0002195315,0.0001950707,0.1183921],"genre_scores_gemma":[0.9914744,0.0002926542,0.002703259,0.0004647059,0.00005268247,0.00001515836,0.00005810926,0.00001882149,0.004920303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006327555,"threshold_uncertainty_score":0.02116776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2734203710729889,"score_gpt":0.4941905009899039,"score_spread":0.220770129916915,"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."}}