{"id":"W2946790488","doi":"10.5194/wes-5-1191-2020","title":"US East Coast synthetic aperture radar wind atlas for offshore wind energy","year":2020,"lang":"en","type":"article","venue":"Wind energy science","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Renewable Energy Laboratory","keywords":"Offshore wind power; Synthetic aperture radar; Submarine pipeline; Geology; Atlas (anatomy); Marine engineering; Side looking airborne radar; Remote sensing; Wind power; Meteorology; Environmental science; Oceanography; Radar; Radar imaging; Aerospace engineering; Geography; Engineering; Radar engineering details","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":[0.0002547743,0.0005286313,0.0001946885,0.001831669,0.0003354278,0.0004184517,0.0003556876,0.0002015334,0.004906218],"category_scores_gemma":[0.0004273014,0.0001264506,0.0001965948,0.003403444,0.00009280079,0.0003055875,0.0004980298,0.0004480877,0.002536278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009333729,"about_ca_system_score_gemma":0.002965434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2973692,"about_ca_topic_score_gemma":0.4060414,"domain_scores_codex":[0.9998585,0.00001272138,0.00001259894,0.00002407697,0.00006537051,0.00002674192],"domain_scores_gemma":[0.9993855,0.00001413171,0.00007145295,0.00007172031,0.0004048315,0.00005233287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001621476,0.00006885787,0.08275296,0.0003375285,0.0000946172,0.0002463526,0.0001753573,0.00560112,0.003213569,0.003638123,0.8353987,0.06831068],"study_design_scores_gemma":[0.00005587178,0.00002892355,0.2604916,0.0001445423,0.0000283179,0.000102426,0.0002678984,0.005141903,0.001110796,0.0009262366,0.7316679,0.00003357188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03548562,0.0006611056,0.004040406,0.0004662411,0.0002410238,0.0002464643,0.9257817,0.00080928,0.0322681],"genre_scores_gemma":[0.05784087,0.0006554541,0.0127969,0.0001967278,0.00003403652,0.0002529197,0.9181277,0.0001457498,0.009949595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2973692,"threshold_uncertainty_score":0.5912765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331766257152137,"score_gpt":0.1895417353387357,"score_spread":0.1762240727672143,"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."}}