{"id":"W4287219816","doi":"10.3390/su14159021","title":"A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wind speed; Robustness (evolution); Generator (circuit theory); Artificial neural network; Noise (video); Artificial intelligence; Machine learning; Algorithm","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":[0.0004453812,0.0001704208,0.0001829884,0.00004048957,0.0003954442,0.00002679784,0.0001365287,0.0000413786,0.00004960824],"category_scores_gemma":[0.0000916066,0.000193256,0.0001225447,0.000126354,0.00003928466,0.000139765,0.00005461656,0.000240719,2.701541e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009671999,"about_ca_system_score_gemma":0.0001541584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001206645,"about_ca_topic_score_gemma":0.000006900686,"domain_scores_codex":[0.9987836,0.00005555046,0.0002609527,0.0002841723,0.000169532,0.0004461579],"domain_scores_gemma":[0.9994444,0.0000746533,0.0000245366,0.0002403895,0.0001376512,0.00007837706],"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":[0.00009673333,0.00002970787,0.002443101,0.00008257742,0.00003747187,0.000004729612,0.001075099,0.9917457,0.000185227,0.0006062912,0.002309679,0.001383753],"study_design_scores_gemma":[0.000380606,0.0000955365,0.0004375827,0.000003433315,0.00002720675,0.00000585581,0.0002531816,0.9869896,0.0003684409,0.009190089,0.002060864,0.0001875929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9075145,0.0001047999,0.08860037,0.00006925624,0.001606182,0.0007501284,0.0002636663,0.0003685596,0.0007225581],"genre_scores_gemma":[0.9981491,0.000002873345,0.0005702697,0.00002292946,0.0006576465,0.00006551688,0.0001777887,0.00003599859,0.0003178953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0906346,"threshold_uncertainty_score":0.788075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253748522798867,"score_gpt":0.2283206470817591,"score_spread":0.2157831618537704,"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."}}