{"id":"W4401760812","doi":"10.2196/60003","title":"Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study","year":2024,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Differential privacy; Preprint; Computer science; Affect (linguistics); Internet privacy; Task (project management); Privacy protection; Privacy software; Information privacy; Computer security; Psychology; World Wide Web; Data mining; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001110513,0.0002316687,0.0003774011,0.0002741721,0.0005393308,0.0001293554,0.00005278984,0.000117911,0.0001251194],"category_scores_gemma":[0.0001135033,0.0002354326,0.00006960597,0.0002688912,0.00003686142,0.0002083703,0.00006461044,0.0006232965,0.00002836303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002610484,"about_ca_system_score_gemma":0.00007528104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004482683,"about_ca_topic_score_gemma":0.0001292999,"domain_scores_codex":[0.9971005,0.001118875,0.0005208249,0.0006309342,0.0001682054,0.0004606246],"domain_scores_gemma":[0.9991713,0.0003900985,0.0001859695,0.00007959198,0.00004476194,0.00012827],"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":[0.001190841,0.001634283,0.2587973,0.002089564,0.0003860816,0.0000246088,0.1819189,0.000007700548,0.003717099,0.00003609372,0.0006046675,0.5495928],"study_design_scores_gemma":[0.01061182,0.006078347,0.8272524,0.002782904,0.0001124115,0.00003870138,0.1086096,0.0420836,0.0004721325,0.0002382396,0.0008938773,0.0008259803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933706,0.0001985834,0.002012207,0.0003141345,0.0005393696,0.003147083,0.00006955071,0.0002529976,0.00009546169],"genre_scores_gemma":[0.9979444,0.00001421177,0.0008176782,0.00006550013,0.0001282146,0.0001816866,0.0006862594,0.00004717768,0.0001148705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5684551,"threshold_uncertainty_score":0.9600665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0917124467296896,"score_gpt":0.434629330076734,"score_spread":0.3429168833470445,"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."}}