{"id":"W4402904865","doi":"10.1167/jov.24.10.631","title":"Visualizing the Other-Race Effect with GAN-based Image Reconstruction","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Race (biology); Computer science; Image (mathematics); Artificial intelligence; Computer vision; Geology; Paleontology","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":[0.003064102,0.00005366613,0.0001135626,0.0001173745,0.0002579676,0.0002078578,0.0001016648,0.0000269707,0.0001590749],"category_scores_gemma":[0.0001708313,0.00002681672,0.000126311,0.0004116508,0.0001000821,0.0002371465,0.000003512498,0.0001531149,0.00001164145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006908833,"about_ca_system_score_gemma":0.0001465327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001114456,"about_ca_topic_score_gemma":0.00007908857,"domain_scores_codex":[0.99867,0.0005769521,0.0001789982,0.0000719093,0.0004196191,0.0000824621],"domain_scores_gemma":[0.9985598,0.00107732,0.0001370958,0.00004495498,0.0001379463,0.00004290495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002127116,0.00005052914,0.002719992,0.00004429714,0.0001531427,0.0000605862,0.00393372,0.001621287,0.01500739,0.003785308,0.001105404,0.9713056],"study_design_scores_gemma":[0.003683698,0.006264833,0.07565735,0.006505609,0.001847068,0.0006994646,0.01042293,0.1915108,0.02944731,0.04106812,0.6316656,0.001227232],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8540108,0.00116527,0.1211748,0.01268121,0.001156318,0.000146168,9.258295e-7,0.00004923012,0.009615209],"genre_scores_gemma":[0.9914057,0.00002166842,0.007740824,0.00007392347,0.0005529082,5.915539e-7,1.730912e-7,0.000006561377,0.0001976387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9700784,"threshold_uncertainty_score":0.2004378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668627298657616,"score_gpt":0.4078190814550394,"score_spread":0.3911328084684633,"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."}}