{"id":"W1979034248","doi":"10.1109/tec.2009.2015973","title":"Annual Wind Speed Estimation Utilizing Constrained Grey Predictor","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Conversion","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wind speed; Estimation; Wind power; Meteorology; Computer science; Environmental science; Control theory (sociology); Engineering; Artificial intelligence; Geography; Electrical engineering; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.00007037622,0.0002260996,0.0001746525,0.0002125263,0.0001702468,0.00002511413,0.0001198331,0.0001562533,0.0001769807],"category_scores_gemma":[0.000003409402,0.0002430465,0.0001046799,0.0002444073,0.00004321544,0.0003635073,6.108066e-7,0.0001968715,0.0000290339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009247992,"about_ca_system_score_gemma":0.00002460393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002725928,"about_ca_topic_score_gemma":0.000007029788,"domain_scores_codex":[0.9990106,0.00002525751,0.000241691,0.0002227458,0.0002112006,0.0002885],"domain_scores_gemma":[0.9995356,0.00005884417,0.00003526327,0.0001852032,0.00004864882,0.0001364755],"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":[0.00004669254,0.00005439077,0.000003247668,0.00001625285,0.00003978551,0.00001357421,0.0002730103,0.9311352,0.01035176,0.0003139513,0.0005533795,0.05719879],"study_design_scores_gemma":[0.001091858,0.0002518334,0.00008792322,0.0001423392,0.00005398455,0.00003169811,0.0001651831,0.8404192,0.1539078,0.0001612111,0.003297537,0.0003894404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2413783,0.00008356795,0.7437338,0.0001396461,0.00349834,0.0001118062,0.0001443056,0.001326245,0.009583985],"genre_scores_gemma":[0.9985141,0.00007793125,0.0007230988,0.0001402445,0.00008386343,9.608659e-7,0.00002384299,0.00002747209,0.0004084493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7571358,"threshold_uncertainty_score":0.9911149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00977078168490298,"score_gpt":0.2038129884050549,"score_spread":0.1940422067201519,"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."}}