{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000862653,0.0008679859,0.0006763735,0.0003839601,0.0002682075,0.0005482518,0.001149076,0.0009259709,0.001357253],"category_scores_gemma":[0.001892384,0.0003975476,0.0005336402,0.0003735496,0.0006289605,0.0008969316,0.0008855943,0.001431072,0.0003019376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005569392,"about_ca_system_score_gemma":0.0004726125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005302674,"about_ca_topic_score_gemma":0.00510224,"domain_scores_codex":[0.9997209,0.00008627612,0.00001125378,0.00008539027,0.00005374189,0.00004247536],"domain_scores_gemma":[0.9993392,0.0004180638,0.00006777612,0.00004071296,0.0001050696,0.00002919808],"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":[0.00002250831,0.000009203046,0.0003900433,0.00001024532,0.00001647485,0.00003355533,0.00001635205,0.9864117,0.0005605487,0.003430736,0.0004673688,0.008631289],"study_design_scores_gemma":[7.220905e-7,0.00000330114,0.00003498371,0.000001127738,0.000002018208,0.000003815508,7.047998e-7,0.999,0.00008497294,0.0008033557,0.0000635038,0.000001454958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03946364,0.0006250093,0.9546998,0.0004802914,0.0001153784,0.00003288127,0.0001712829,0.0005184603,0.003893337],"genre_scores_gemma":[0.9454165,0.000362356,0.04751251,0.000276286,0.0000731639,0.0001039939,0.0003024888,0.00006854561,0.005884079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005302674,"threshold_uncertainty_score":0.01054358,"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."}}