{"id":"W4210357218","doi":"10.1145/3506701","title":"Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","topic":"Mental Health via Writing","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Contrast (vision); Mental health; Artificial intelligence; Computer science; Feature (linguistics); Machine learning; Set (abstract data type); The Internet; Fuzzy logic; Data mining; Psychology; Psychiatry; World Wide Web; Linguistics","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":[0.0004206113,0.0006135617,0.0004981744,0.0008322837,0.000401402,0.0006750723,0.0009118267,0.0008178564,0.00243951],"category_scores_gemma":[0.001294554,0.0002118213,0.0007293904,0.0005246861,0.0003198067,0.001055647,0.0006044905,0.0009161357,0.0003939388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009589238,"about_ca_system_score_gemma":0.0006214701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007025667,"about_ca_topic_score_gemma":0.007359877,"domain_scores_codex":[0.9997769,0.00004583654,0.00001624897,0.00007647307,0.00004374317,0.00004088689],"domain_scores_gemma":[0.9997334,0.0001320087,0.00002671793,0.00001793247,0.00007373651,0.00001620555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005469011,0.0005770287,0.009124928,0.000155425,0.0002488595,0.0003746644,0.0002512397,0.3045536,0.01671727,0.008389597,0.004698659,0.6543618],"study_design_scores_gemma":[0.000005321187,0.00004477392,0.0006809522,0.000006518622,0.00002639612,0.00003157136,0.0000171376,0.9950873,0.001304857,0.002499523,0.0002898879,0.000005671314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2281419,0.00165478,0.7602321,0.001116631,0.0002299109,0.0001780364,0.0004547781,0.00143559,0.006556262],"genre_scores_gemma":[0.9339008,0.0003418168,0.05976514,0.0002600641,0.00007444994,0.0001195661,0.0004170208,0.00002679071,0.005094387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007025667,"threshold_uncertainty_score":0.01396954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459727850774725,"score_gpt":0.3173217739189699,"score_spread":0.3027244954112226,"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."}}