{"id":"W4292348386","doi":"10.1109/eeeic/icpseurope54979.2022.9854682","title":"Low Voltage Network State Estimation: RSE's Experimental Validation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&amp;CPS Europe)","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Power Generation","funders":"","keywords":"Observability; Phasor; Smart grid; Context (archaeology); Units of measurement; Phasor measurement unit; Computer science; Electric power system; Distributed generation; State (computer science); Estimation; Control theory (sociology); State variable; Reliability engineering; Power (physics); Control engineering; Engineering; Control (management); Mathematics","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":[0.001594705,0.0007654396,0.0005029608,0.0006870531,0.0004373559,0.0006447849,0.001050078,0.0009638563,0.004035275],"category_scores_gemma":[0.005171964,0.0001934403,0.0003307658,0.0006341653,0.0006189531,0.0009418238,0.0007435495,0.0007001277,0.001041284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004173074,"about_ca_system_score_gemma":0.0003240671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002487946,"about_ca_topic_score_gemma":0.002328962,"domain_scores_codex":[0.9986061,0.0004064662,0.0000995097,0.0002427838,0.0005293392,0.0001157612],"domain_scores_gemma":[0.9960203,0.001512213,0.0002798997,0.0009820526,0.001123417,0.00008211177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00305107,0.004094496,0.01928673,0.001420081,0.0003028672,0.0005703694,0.001033917,0.4235436,0.3095163,0.006423584,0.006015148,0.224742],"study_design_scores_gemma":[0.0002436579,0.003877189,0.01014697,0.0001078243,0.00007644744,0.0002169758,0.0002994631,0.734614,0.2427841,0.001501482,0.006054952,0.00007714135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8086005,0.0004349077,0.1742093,0.0002245637,0.0002372385,0.0004806021,0.00191767,0.003635995,0.01025918],"genre_scores_gemma":[0.9828629,0.00008745147,0.01453192,0.0000347525,0.000007254142,0.000153109,0.0006595348,0.00005927525,0.001603821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004035275,"threshold_uncertainty_score":0.01349932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03666691431889157,"score_gpt":0.2307465885669791,"score_spread":0.1940796742480875,"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."}}