{"id":"W2047062376","doi":"10.1177/154193121005401314","title":"Predicting operator mental workload using a time-based algorithm","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Defence Research and Development Canada","funders":"","keywords":"Workload; Computer science; Task (project management); Milgram experiment; Algorithm; Operator (biology); Context (archaeology); Distributed computing; Operating system","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":[0.0004829817,0.0002196375,0.0002491994,0.00004315848,0.0008872168,0.0001196235,0.0002716387,0.0001853691,0.0003120866],"category_scores_gemma":[0.00004661213,0.0001753281,0.0002432033,0.0001121267,0.0002033558,0.0002794924,0.0001458795,0.0005038514,0.000006400202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000065628,"about_ca_system_score_gemma":0.00002764281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001406876,"about_ca_topic_score_gemma":0.00000580983,"domain_scores_codex":[0.998779,0.00001384782,0.0004513947,0.0003180707,0.0001471118,0.0002906037],"domain_scores_gemma":[0.9991164,0.00007454776,0.0004280665,0.0001044285,0.0001792905,0.00009722116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000156578,0.0005307727,0.4739103,0.0001819179,0.0006562042,3.182959e-7,0.1450739,0.00005318686,0.3613681,0.002548671,0.008616349,0.006903731],"study_design_scores_gemma":[0.008510376,0.0005994862,0.3861813,0.001634699,0.0006031585,0.00008362845,0.2214287,0.2513255,0.1063485,0.0005830904,0.01939228,0.003309265],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996668,0.00003087516,0.00001402638,0.00008312659,0.000808018,0.0002204578,0.00006135403,0.00008594755,0.002028192],"genre_scores_gemma":[0.9956416,0.000002824393,0.003432909,0.0001659674,0.0002836478,0.000009085577,0.000004643432,0.00003344796,0.0004258282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2550197,"threshold_uncertainty_score":0.7149672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595457841024001,"score_gpt":0.2879879089090372,"score_spread":0.2720333304987972,"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."}}