{"id":"W2900270437","doi":"10.1108/ijesm-07-2018-0017","title":"Determining the efficacy of consolidating municipal electric utilities in Ontario, Canada","year":2018,"lang":"en","type":"article","venue":"International Journal of Energy Sector Management","topic":"Electric Power System Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Equity (law); Consolidation (business); Shareholder; Electricity; Finance; Business; Operational efficiency; Debt; Mergers and acquisitions; Government (linguistics); Electric power distribution; Electric utility; Wilcoxon signed-rank test; Accounting; Economics; Marketing; Engineering; Economic growth; Corporate governance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001924297,0.00009008959,0.0001355972,0.0002588802,0.00001977211,0.00002442479,0.0004197018,0.00002076705,0.00008584321],"category_scores_gemma":[0.00003949299,0.00007514411,0.00003923593,0.0001620774,0.0000167487,0.00009503666,0.00003957524,0.0001093435,2.918696e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007367548,"about_ca_system_score_gemma":0.0001186346,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2376536,"about_ca_topic_score_gemma":0.8266624,"domain_scores_codex":[0.998862,0.00003574109,0.0004949322,0.00006253683,0.0004035546,0.0001412795],"domain_scores_gemma":[0.9993483,0.0001251756,0.0001963453,0.00009710827,0.0002087601,0.00002431142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004403404,0.0003593665,0.120478,0.0001180138,0.004315163,0.0004725963,0.006553381,0.7910548,0.001960834,0.01997568,0.01069488,0.04357695],"study_design_scores_gemma":[0.007992759,0.000697962,0.5882508,0.001284211,0.0002181324,0.0002128006,0.001220013,0.2810126,0.02372642,0.0004416793,0.09395842,0.0009842635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787798,0.0001154088,0.003576564,0.00003256104,0.001669623,0.00005266427,0.000001165576,0.000009909344,0.01576231],"genre_scores_gemma":[0.9992902,0.00002345008,0.0003194686,0.00005369249,0.000134441,0.000002461453,0.000002307983,0.00001190479,0.0001621074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5890089,"threshold_uncertainty_score":0.767423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009803151229255328,"score_gpt":0.2084233762538426,"score_spread":0.1986202250245873,"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."}}