{"id":"W4403755532","doi":"10.2172/2473210","title":"WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset","year":2024,"lang":"en","type":"report","venue":"","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Renewable Energy Laboratory; Argonne National Laboratory; Office of Energy Efficiency; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy; Wind Energy Technologies Office; National Science Foundation","keywords":"Term (time); Computer science; Meteorology; Environmental science; Geography; Physics; Astronomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089404,0.00159863,0.001182198,0.0008638967,0.0005886545,0.001321684,0.002851047,0.001685889,0.01071466],"category_scores_gemma":[0.002715806,0.0005244153,0.001366318,0.002114969,0.000305711,0.001352607,0.001256184,0.002268653,0.01195938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005883393,"about_ca_system_score_gemma":0.001441047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02923173,"about_ca_topic_score_gemma":0.05218663,"domain_scores_codex":[0.9993727,0.0001347678,0.00008125461,0.0001517816,0.0001736911,0.00008582172],"domain_scores_gemma":[0.9990187,0.0001439365,0.00007904846,0.0002928236,0.0003771439,0.000088295],"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":[0.0002126698,0.0001040132,0.006149658,0.0004899444,0.0002698408,0.0001299085,0.00005238867,0.02055485,0.0009421771,0.00156876,0.9607803,0.008745592],"study_design_scores_gemma":[0.002170978,0.0001120921,0.03570791,0.0003783518,0.0001870767,0.0001633412,0.0002169015,0.1159718,0.002649521,0.00882057,0.8333368,0.0002847464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004906153,0.000216962,0.002862875,0.0002744625,0.0002278896,0.00006221071,0.9858371,0.003756093,0.001856197],"genre_scores_gemma":[0.006759187,0.00006028041,0.002628844,0.00007522315,0.00002495742,0.0001097317,0.9895549,0.0003115585,0.000475313],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02923173,"threshold_uncertainty_score":0.05812317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03495570594029042,"score_gpt":0.2802633006479258,"score_spread":0.2453075947076354,"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."}}