{"id":"W2115617985","doi":"10.1002/we.1860","title":"Digital tuft analysis of stall on operational wind turbines","year":2015,"lang":"en","type":"article","venue":"Wind Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tuft; Stall (fluid mechanics); Wind power; Marine engineering; Environmental science; Aerospace engineering; Engineering; Meteorology; Electrical engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001876929,0.0003333198,0.0003371998,0.0006033861,0.0002821511,0.000579038,0.0004554919,0.0004387933,0.003510229],"category_scores_gemma":[0.001198973,0.0001396033,0.0003253028,0.0002630233,0.0006106183,0.0005435816,0.0004323176,0.0003270812,0.0002352406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003904817,"about_ca_system_score_gemma":0.0002342124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002841157,"about_ca_topic_score_gemma":0.002497698,"domain_scores_codex":[0.9999036,0.00001468332,0.000003949655,0.000012931,0.00003831455,0.00002658638],"domain_scores_gemma":[0.999721,0.0001291228,0.000033815,0.00003147177,0.00006653685,0.00001804704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004547857,0.00008295725,0.005460935,0.0001800575,0.0000458502,0.0005480048,0.0001750193,0.8984048,0.0146203,0.0324855,0.00169088,0.04585085],"study_design_scores_gemma":[0.000002763753,0.0000187005,0.00112083,0.000006142219,0.000005399537,0.00003063092,0.00002746099,0.9967712,0.0008951894,0.0008140037,0.0003044565,0.000003245058],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7617754,0.0009766796,0.2084781,0.0002187479,0.0002231651,0.0000370045,0.0001785171,0.0004201133,0.02769225],"genre_scores_gemma":[0.9949836,0.0001212468,0.001351662,0.00001151671,0.00001888801,0.00000400799,0.00004215689,0.00002517584,0.00344185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003510229,"threshold_uncertainty_score":0.01174289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718790486577912,"score_gpt":0.2191975982437855,"score_spread":0.2020096933780063,"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."}}