{"id":"W4412536513","doi":"10.1109/access.2025.3591059","title":"Forecasting Transmission Line Loss Using a Cluster-Based Refinement Framework and Scheduled Outage Data","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Thermal Analysis in Power Transmission","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Electric System Operator","keywords":"Computer science; Cluster (spacecraft); Data loss; Data modeling; Line (geometry); Transmission line; Computer network; Telecommunications; Database; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924158,0.0002321607,0.0002809324,0.000194027,0.0001389854,0.0001575273,0.0007163102,0.0001539144,0.00007721412],"category_scores_gemma":[0.00003788962,0.0002081366,0.00005211458,0.000467468,0.0000393174,0.0003638197,0.000117541,0.0003150985,0.00000149686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004997294,"about_ca_system_score_gemma":0.000032884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003321145,"about_ca_topic_score_gemma":0.00001205788,"domain_scores_codex":[0.9987043,0.00004198336,0.0003852166,0.0003865813,0.0001949723,0.000286908],"domain_scores_gemma":[0.9990141,0.0001473936,0.00004569425,0.0006598756,0.00003577052,0.00009711435],"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":[0.0001862044,0.0001364666,0.003442415,0.001622091,0.0003070044,0.00005229044,0.0002593548,0.8230469,0.02327103,0.00005420813,0.0002829942,0.1473391],"study_design_scores_gemma":[0.0006142083,0.00001125359,0.0001271566,0.001075125,0.0001557674,0.000002154646,0.00001186324,0.9749715,0.02001681,0.0003235777,0.002470143,0.0002204486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3202436,0.0006419956,0.6781276,0.0002170779,0.0002852683,0.000141515,0.00001945483,0.0001485989,0.0001748581],"genre_scores_gemma":[0.9672126,0.0000537702,0.03231945,0.0002186887,0.00008556611,0.000007049951,0.00003525908,0.00003686457,0.00003072502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.646969,"threshold_uncertainty_score":0.8487563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07632480623146573,"score_gpt":0.3429562765998372,"score_spread":0.2666314703683715,"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."}}