{"id":"W7131813647","doi":"10.1109/metrocon66796.2025.11405823","title":"Navigating Successful Automation on Challenged Programs: A Case Study and Practical Solutions","year":2025,"lang":"","type":"article","venue":"","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Automation; Leverage (statistics); Plan (archaeology); Process automation system; ISA100.11a","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005160776,0.0008215025,0.0003291731,0.001150115,0.006457323,0.003899315,0.003406756,0.004293941,0.003585764],"category_scores_gemma":[0.01324229,0.0004466433,0.0006751814,0.001266521,0.003304917,0.003969296,0.004232566,0.003547906,0.0008233426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00321122,"about_ca_system_score_gemma":0.004521159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006267446,"about_ca_topic_score_gemma":0.01649508,"domain_scores_codex":[0.9911424,0.00499269,0.0002449622,0.0005882516,0.001421683,0.001609962],"domain_scores_gemma":[0.9897933,0.004701324,0.0008066897,0.001053168,0.001377779,0.00226774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0009834964,0.02319806,0.06792609,0.001361998,0.0001592961,0.06158117,0.1778068,0.07109804,0.0142242,0.1065245,0.03811282,0.4370235],"study_design_scores_gemma":[0.0005393129,0.00820686,0.0394608,0.001431291,0.0001937398,0.02066183,0.3074231,0.1942055,0.03974262,0.05440833,0.3331049,0.0006217288],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8949853,0.0002071138,0.06058318,0.00418538,0.0001112906,0.0009410288,0.000137369,0.000693884,0.03815549],"genre_scores_gemma":[0.9584875,0.0002218412,0.03229294,0.0003363396,0.00002773566,0.0002745655,0.0001063824,0.0001308591,0.00812193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006457323,"threshold_uncertainty_score":0.02729315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03151207639867434,"score_gpt":0.344213825548044,"score_spread":0.3127017491493697,"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."}}