{"id":"W4297999823","doi":"10.1145/3563950","title":"Synthetic Behavior Sequence Generation Using Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Computing for Healthcare","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Similarity (geometry); Generative grammar; Sequence (biology); Machine learning; Adversarial system; Reinforcement learning; Artificial intelligence; Variety (cybernetics); Function (biology); Data mining; Population","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":[0.0008688449,0.0007755015,0.0004630205,0.0003772326,0.0001787796,0.0003278399,0.0008454621,0.0006844808,0.001250904],"category_scores_gemma":[0.003356968,0.0002866416,0.0004716653,0.0002674749,0.0006388814,0.0005410389,0.0006771026,0.0009824375,0.0002323962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006895774,"about_ca_system_score_gemma":0.0004338574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003535233,"about_ca_topic_score_gemma":0.004107311,"domain_scores_codex":[0.9996332,0.0001551454,0.0000124861,0.00009126116,0.00006603164,0.00004184417],"domain_scores_gemma":[0.9978843,0.001610171,0.0001372694,0.0001519619,0.0001489241,0.00006727596],"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":[0.00005945501,0.0000328545,0.0008849482,0.00002089162,0.00001489067,0.00005927791,0.0000264483,0.9825781,0.0009182246,0.003121982,0.0006942578,0.01158873],"study_design_scores_gemma":[0.000003314352,0.000008828297,0.00007429752,0.000001641371,0.000001305781,0.00000838349,0.000002267765,0.9982412,0.0002692273,0.00128719,0.0001007712,0.000001635895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1762936,0.0004140216,0.8171992,0.0005926018,0.0001168217,0.0002020482,0.000486107,0.001156898,0.003538653],"genre_scores_gemma":[0.9307644,0.0001429987,0.06491727,0.0002174968,0.00003235239,0.0001931347,0.0008404377,0.00007572387,0.002816196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003535233,"threshold_uncertainty_score":0.007029355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1591477138121235,"score_gpt":0.352951869221925,"score_spread":0.1938041554098015,"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."}}