{"id":"W4206549633","doi":"10.2139/ssrn.4006226","title":"An Investigation of How Normalisation and Local Modelling Techniques Confound Machine Learning Performance in a Mental Health Study","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mental health; Machine learning; Computer science; Psychology; Artificial intelligence; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005083376,0.0001093655,0.0001740832,0.0002825641,0.0005887047,0.00006218185,0.0003530893,0.00002224458,0.000002008656],"category_scores_gemma":[0.000008927903,0.0001143644,0.00001831322,0.0003388243,0.00003390973,0.0006355791,0.0001216606,0.002301492,1.043489e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079531,"about_ca_system_score_gemma":0.001203956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001347799,"about_ca_topic_score_gemma":0.001036855,"domain_scores_codex":[0.9973794,0.0009073711,0.0003249269,0.0002319258,0.0004120461,0.0007443743],"domain_scores_gemma":[0.9993618,0.00002801135,0.0003426674,0.000144474,0.00004812604,0.0000749763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005707058,0.0001102175,0.7069327,0.00004153671,0.00001834604,0.000002033211,0.02326853,0.15013,0.00008612814,0.01497301,0.000001022136,0.1043794],"study_design_scores_gemma":[0.0004289265,0.003973216,0.007501923,0.00001820974,0.00000188833,0.000253968,0.005301828,0.979165,0.00003349482,0.003165114,0.00004847363,0.0001079884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7857991,0.0005938489,0.2118263,0.001439993,0.00004066812,0.0002520737,6.219856e-7,0.00004248294,0.000004954592],"genre_scores_gemma":[0.9969979,0.0004494138,0.00239772,0.00007532314,0.00002567934,0.00001528006,0.000009958555,0.00001102339,0.00001773849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.829035,"threshold_uncertainty_score":0.9998966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01828949677056678,"score_gpt":0.27884858136339,"score_spread":0.2605590845928232,"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."}}