{"id":"W4413231910","doi":"10.20944/preprints202508.0416.v1","title":"Monitoring Visual Fatigue with Eye Tracking in a Pharmaceutical Packing Area","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Canadian Institute of Steel Construction","keywords":"Workload; Eye tracking; Workflow; Computer science; Visual search; Fixation (population genetics); Eye movement; Visual inspection; Adaptation (eye); Artificial intelligence; Human–computer interaction; Computer vision; Psychology; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005270042,0.0003160682,0.000336463,0.0006305794,0.0002176961,0.0005228718,0.0002266683,0.0005642699,0.0009057463],"category_scores_gemma":[0.001782566,0.000175025,0.000229554,0.0003788713,0.0001752354,0.0003169215,0.0004603186,0.0002192616,0.0002581085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806109,"about_ca_system_score_gemma":0.0002722953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793158,"about_ca_topic_score_gemma":0.00316819,"domain_scores_codex":[0.999481,0.0001589175,0.00002722952,0.000147226,0.0001323405,0.00005316825],"domain_scores_gemma":[0.999155,0.0003262733,0.0002236939,0.00005318489,0.00018175,0.00006006452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003367025,0.001029854,0.4012258,0.001194823,0.0002849256,0.0006693234,0.004723751,0.006360067,0.403576,0.0002660884,0.001688337,0.175614],"study_design_scores_gemma":[0.00005240533,0.002561038,0.9593844,0.00007951724,0.0001118854,0.0007711424,0.001173049,0.01376363,0.02046186,0.0002680121,0.001315526,0.00005738768],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892351,0.0002044446,0.009527943,0.00004610445,0.0000108051,0.00004984148,0.0002708435,0.00006349472,0.0005912836],"genre_scores_gemma":[0.9884354,0.000196727,0.01044984,0.00006945582,0.00001953968,0.00006263774,0.0002432846,0.00001229254,0.0005107955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001793158,"threshold_uncertainty_score":0.003565431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1876941241517714,"score_gpt":0.4735211492456934,"score_spread":0.285827025093922,"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."}}