{"id":"W4392056957","doi":"10.21203/rs.3.rs-3970681/v1","title":"Emotion-Aware Face De-identification with Generative Adversarial Networks","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Adversarial system; Generative grammar; Identification (biology); Face (sociological concept); Computer science; Artificial intelligence; Cognitive science; Psychology; Linguistics; Philosophy","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00158289,0.0002418811,0.0002660082,0.000600619,0.0003405069,0.00158361,0.001059931,0.0003053629,0.0001077402],"category_scores_gemma":[0.00009843153,0.00020485,0.0001920054,0.00132533,0.0001236922,0.000185045,0.001730777,0.001872822,0.0004335779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004231939,"about_ca_system_score_gemma":0.0008323659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002100801,"about_ca_topic_score_gemma":0.0001281155,"domain_scores_codex":[0.9961923,0.0007413602,0.0002930676,0.00103087,0.001166853,0.0005755842],"domain_scores_gemma":[0.9976296,0.0001771561,0.00009735052,0.0009231809,0.0009422888,0.0002303536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001309742,0.0005819356,0.000747653,0.002518122,0.001547842,0.0006182491,0.01101744,0.6923391,0.00053453,0.03052163,0.0612816,0.1981609],"study_design_scores_gemma":[0.0001702213,0.0000851926,0.0003442634,0.0006154277,0.00003298793,0.000007671107,0.0006434182,0.9890333,0.0005571398,0.007490808,0.0007347885,0.0002847911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004426134,0.0006275783,0.9881565,0.004278218,0.0004664871,0.0006417755,0.00005929728,0.0002862938,0.001057745],"genre_scores_gemma":[0.9900415,0.0004128983,0.004481707,0.00005321694,0.0006025518,0.0002401397,0.0003106014,0.00003312993,0.003824271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9856154,"threshold_uncertainty_score":0.9994528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04827290496959803,"score_gpt":0.3655764144425613,"score_spread":0.3173035094729633,"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."}}