{"id":"W4391409190","doi":"10.1109/wsc60868.2023.10407941","title":"Simulating Technician Populations with Tandem Analytic and Discrete Event Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Technician; Computer science; Notional amount; Apprenticeship; Population; Workforce; Workforce planning; Work (physics); Industrial engineering; Simulation; Operations research; Mathematics; Engineering; Mechanical engineering","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":[0.000744334,0.0001002813,0.0001576567,0.0003093061,0.000310497,0.000167443,0.0002098621,0.00004635168,0.0001123141],"category_scores_gemma":[0.0001671693,0.000064236,0.00004599946,0.001928532,0.0000549805,0.0002834599,0.000104507,0.00007003947,0.00005727768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000190617,"about_ca_system_score_gemma":0.00002026689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004887526,"about_ca_topic_score_gemma":0.0001013451,"domain_scores_codex":[0.9984097,0.00003054598,0.0004099049,0.0003661201,0.0006088552,0.0001748288],"domain_scores_gemma":[0.9988139,0.0003796733,0.0001167115,0.0004544952,0.0001466522,0.00008851519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006301113,0.00002321866,0.05154999,0.000002201463,0.00001267166,0.000002642611,0.0002688997,0.6534517,0.0001566247,0.2557017,0.002590096,0.03623391],"study_design_scores_gemma":[0.0000863364,0.00002024661,0.01934018,0.000007086246,0.000007344148,0.00000145885,0.000311058,0.7382973,0.00003176187,0.2408647,0.0009429425,0.0000895156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.203602,0.00001342032,0.7795182,0.002257842,0.00001757313,0.0004321366,0.00001057481,0.0006481994,0.01350002],"genre_scores_gemma":[0.9868856,0.000003775408,0.009336906,0.0001138233,0.00001977286,0.00003374093,0.00001000427,0.00001179079,0.003584608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7832836,"threshold_uncertainty_score":0.2619468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2409506691694236,"score_gpt":0.4651285341943015,"score_spread":0.2241778650248779,"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."}}