{"id":"W4413616956","doi":"10.2196/73896","title":"Exploring Subjective Well-Being in Human-Machine Interaction: Protocol for a Mixed Methods, Cross-Sectional Analysis in Manufacturing 5.0","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Technostress in Professional Settings","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Cross-sectional study; Protocol (science); Computer science; Psychology; Medicine; World Wide Web; Alternative medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007007027,0.0003257279,0.0005476233,0.003991139,0.0005276199,0.0002306225,0.0008732185,0.0003335376,0.0009173359],"category_scores_gemma":[0.0005733939,0.0003174837,0.0002643965,0.003033626,0.0002147914,0.0005831194,0.000661533,0.002393297,0.00002939292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009567198,"about_ca_system_score_gemma":0.0001300524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005245115,"about_ca_topic_score_gemma":0.0004530171,"domain_scores_codex":[0.993893,0.001990088,0.001141387,0.001221044,0.0006566898,0.001097806],"domain_scores_gemma":[0.9960034,0.002574269,0.0002433318,0.0007792424,0.0002999373,0.00009977047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005710653,0.001944943,0.93775,0.001045924,0.0003989669,0.00006899921,0.002007357,0.0002457217,0.002569555,0.0191707,0.0007575974,0.02832961],"study_design_scores_gemma":[0.005914972,0.0003732084,0.8650119,0.002327956,0.000005919304,0.000003220559,0.001057969,0.0002215048,0.05657032,0.0211764,0.0469727,0.000363912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.02943615,9.360751e-7,0.001573662,0.0001787239,0.00006888958,0.9540063,0.000009744414,0.0001595851,0.01456604],"genre_scores_gemma":[0.08552811,4.517548e-8,0.002690337,0.00002615925,0.0001074875,0.9091877,0.00001593817,0.00004413761,0.0024001],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.07273806,"threshold_uncertainty_score":0.9999959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3730754204390382,"score_gpt":0.6703667926546105,"score_spread":0.2972913722155723,"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."}}