{"id":"W6947667163","doi":"10.48448/46e4-x248","title":"DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Depression (economics); Task (project management); Domain (mathematical analysis); Pattern recognition (psychology)","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":[0.002595574,0.002429104,0.0008831936,0.001527496,0.000721796,0.002299087,0.002399345,0.00320379,0.0400511],"category_scores_gemma":[0.01114771,0.0006836437,0.00130463,0.0007882587,0.0003793894,0.002230558,0.003330746,0.00243082,0.05267851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009678017,"about_ca_system_score_gemma":0.001286857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01055095,"about_ca_topic_score_gemma":0.01826318,"domain_scores_codex":[0.9983677,0.0004991285,0.0001058111,0.0004800583,0.0003809511,0.0001662946],"domain_scores_gemma":[0.9961299,0.001797879,0.0001469607,0.0007079861,0.00080853,0.0004086457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006551369,0.0003125712,0.004858273,0.0007131771,0.0001165775,0.0002777278,0.0001941619,0.009434405,0.005205839,0.001422399,0.8190608,0.1577488],"study_design_scores_gemma":[0.0006679965,0.0007096113,0.01707019,0.0004247304,0.000201253,0.0007408355,0.0005367608,0.541219,0.02927156,0.02458471,0.3843266,0.0002466898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05304389,0.002045115,0.2608683,0.008736542,0.003640653,0.001683053,0.4452882,0.1888001,0.0358941],"genre_scores_gemma":[0.1764532,0.0008385804,0.1705736,0.002435872,0.0007481003,0.002058709,0.5918213,0.007063589,0.04800718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0400511,"threshold_uncertainty_score":0.1339843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05989680178429245,"score_gpt":0.2723282906971519,"score_spread":0.2124314889128594,"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."}}