{"id":"W4411832621","doi":"10.1049/icp.2025.2334","title":"Building a hydro-generator rotor temperature virtual sensor using machine-learning","year":2025,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Rotor (electric); Generator (circuit theory); Computer science; Artificial intelligence; Control engineering; Automotive engineering; Mechanical engineering; Engineering; Physics; Power (physics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001929228,0.0003045269,0.0002867065,0.0002460715,0.0002260757,0.0002985325,0.0002248278,0.00021348,0.0000549779],"category_scores_gemma":[0.0001121841,0.0003011752,0.00007261228,0.0004933001,0.00005190981,0.0003536854,0.00007502345,0.0006429896,0.00000956794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001068686,"about_ca_system_score_gemma":0.00005519979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002197234,"about_ca_topic_score_gemma":0.000001943966,"domain_scores_codex":[0.9987507,0.00001080637,0.0002910451,0.0003931343,0.0001835711,0.0003707421],"domain_scores_gemma":[0.9994689,0.00001138584,0.00005312666,0.0001118575,0.0002653251,0.00008945804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000166772,0.00001946514,0.002392842,0.0001971438,0.00006415306,0.000003266875,0.0004205245,0.0007033758,0.9825248,0.004591233,0.001456112,0.007610461],"study_design_scores_gemma":[0.0002387823,0.00005812197,0.00008408247,0.000260712,0.0000363315,0.00002146328,0.0002805386,0.09205924,0.8911108,0.0006743987,0.01474499,0.000430562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909861,0.000347639,0.00234767,0.0002297934,0.000638648,0.0003109767,0.00001039033,0.001988709,0.003140076],"genre_scores_gemma":[0.9821907,0.00009977263,0.0162674,0.00009996827,0.000297046,0.00005445978,0.000004236029,0.0000436898,0.0009427732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09141397,"threshold_uncertainty_score":0.999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050217587853907,"score_gpt":0.2368243397356221,"score_spread":0.2263221638570831,"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."}}