{"id":"W2793649869","doi":"10.1016/j.dib.2018.03.015","title":"Dataset for case studies of hydropower unit commitment","year":2018,"lang":"en","type":"article","venue":"Data in Brief","topic":"Electric Power System Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Hydropower; Spillage; Computer science; Quarter (Canadian coin); Power (physics); Operations research; Power system simulation; Environmental science; Engineering; Electric power system; Electrical engineering; Waste management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838003,0.00007447418,0.0001451553,0.00006607646,0.00002414061,0.000007760264,0.0002407108,0.00002805205,0.00001544042],"category_scores_gemma":[0.0001137388,0.00007457078,0.000006341475,0.0001725918,0.00004240424,0.0001726256,0.0001153878,0.00003526929,0.000008793291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003277034,"about_ca_system_score_gemma":0.000008348933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009017742,"about_ca_topic_score_gemma":0.0004381766,"domain_scores_codex":[0.9994338,0.00001754306,0.0002188512,0.0001347946,0.00006580589,0.0001292563],"domain_scores_gemma":[0.9990465,0.0000968513,0.00003141359,0.0007648253,0.00004106214,0.00001935698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002425574,0.00008002386,0.0006427385,0.0004053703,0.0002145276,0.0001401186,0.0005923887,0.003191332,0.0002010375,0.0003044721,0.9922411,0.001962641],"study_design_scores_gemma":[0.001675222,0.0002604318,0.0001253907,0.0001464727,0.00007760774,0.000321046,0.0003315983,0.4131427,0.003524596,0.00007978322,0.5799386,0.0003764898],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2770715,0.01054414,0.3420468,0.0004117326,0.005259708,0.005527651,0.3557232,0.0005162435,0.002898903],"genre_scores_gemma":[0.9708588,0.0000935381,0.005964624,0.00004538162,0.00009028598,0.00003251057,0.0228613,0.00002701659,0.00002654031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6937873,"threshold_uncertainty_score":0.3040908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0796497880481104,"score_gpt":0.3505996372898815,"score_spread":0.2709498492417711,"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."}}