{"id":"W4379231384","doi":"10.56958/jesi.2020.5.4.5","title":"Wind resource assessment system based on time-scale-dependent roughness","year":2020,"lang":"en","type":"article","venue":"Journal of Engineering Sciences and Innovation","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Wind speed; Wind power; Context (archaeology); Scale (ratio); Environmental science; Meteorology; Wind resource assessment; Computer science; Resource (disambiguation); Wind direction; Geography; Engineering; Cartography","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":[0.0004804451,0.00009098835,0.0001332383,0.0002412788,0.00005554231,0.00007637887,0.0001049833,0.00003429194,0.000005347597],"category_scores_gemma":[0.00002905653,0.00007534474,0.00002108459,0.0008232709,0.0000147183,0.0001971961,0.000009126725,0.0001630911,9.684292e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004558867,"about_ca_system_score_gemma":0.00002209027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.114483e-7,"about_ca_topic_score_gemma":3.551595e-8,"domain_scores_codex":[0.9991951,0.000008005111,0.0003151433,0.00008064841,0.0002825952,0.0001185024],"domain_scores_gemma":[0.9997195,0.00004146403,0.00009167015,0.00003935825,0.00005929112,0.0000487433],"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":[0.000003546155,0.00000372237,0.0002413527,0.00006335728,0.000005870891,0.000003937032,0.00006407131,0.9855561,0.01147055,0.001246513,0.0001120593,0.00122891],"study_design_scores_gemma":[0.0001910387,0.0001941015,0.0008370866,0.0002176949,0.000005721138,0.00001557177,0.00006778804,0.9935994,0.002334277,0.00000236791,0.00244275,0.00009224383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8963102,0.00009727862,0.09627525,0.0004476631,0.0005045129,0.00005445036,0.000002644945,0.0001103606,0.006197643],"genre_scores_gemma":[0.9956243,0.00000252598,0.004036735,0.00008133097,0.0002362562,4.225347e-7,9.830442e-7,0.00001007441,0.000007375029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0993141,"threshold_uncertainty_score":0.3072469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430301105447777,"score_gpt":0.2225070478864829,"score_spread":0.2082040368320052,"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."}}