{"id":"W4413030722","doi":"10.1101/2025.08.02.25332538","title":"One does not fit all: Detecting work-related stress from mouse, keyboard, and cardiac data in the field","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Movement Disorders","funders":"Eidgenössische Technische Hochschule Zürich","keywords":"Field (mathematics); Work (physics); Stress (linguistics); Psychology; Computer science; Engineering; Mathematics; Mechanical engineering; Philosophy","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001059545,0.0004398983,0.0006734758,0.0002106586,0.0001131601,0.0001652148,0.00183875,0.00121116,0.0003737251],"category_scores_gemma":[0.0005350895,0.0003224945,0.0001332791,0.0003389842,0.0000899027,0.00006543229,0.001716444,0.00299145,0.00004615034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003014797,"about_ca_system_score_gemma":0.00003951829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005247069,"about_ca_topic_score_gemma":0.001013026,"domain_scores_codex":[0.9963473,0.0008366389,0.0006860458,0.001312463,0.0003187484,0.0004987816],"domain_scores_gemma":[0.9934497,0.003110613,0.0002600763,0.003058284,0.00003940195,0.00008188876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003232547,0.0003572779,0.8464338,0.00006231438,0.00351978,0.0001438025,0.01370397,0.0001866852,0.00008487285,0.0004267771,0.004691742,0.1300658],"study_design_scores_gemma":[0.009241197,0.0003046477,0.9243116,0.0135805,0.005974576,0.000003316868,0.0115303,0.0006327098,0.007160413,0.003484578,0.01768214,0.006094066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816248,0.00431396,0.00007095525,0.003678634,0.003622496,0.0007637204,0.001007808,0.0001556635,0.004761895],"genre_scores_gemma":[0.9966081,0.0004692815,0.0002324994,0.0009036939,0.0002760393,0.0001372437,0.0005497451,0.00004340106,0.0007800385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1239717,"threshold_uncertainty_score":0.9999227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05805586561881297,"score_gpt":0.3296962674842716,"score_spread":0.2716404018654586,"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."}}