{"id":"W2969768536","doi":"10.35378/gujs.459840","title":"Wind Speed Clustering Using Linkage-Ward Method: A Case Study of Khaaf, Iran","year":2019,"lang":"en","type":"article","venue":"GAZI UNIVERSITY JOURNAL OF SCIENCE","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wind power; Wind speed; Cluster analysis; Probabilistic logic; Renewable energy; Meteorology; Environmental science; Electricity; Computer science; Engineering; Geography; Electrical engineering; Artificial intelligence","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":[0.0007971341,0.00009055331,0.0002084111,0.0004285403,0.0001419668,0.00002599604,0.000355134,0.00003088953,0.00002263329],"category_scores_gemma":[0.00002113276,0.00009131364,0.00005918745,0.0006947233,0.000104815,0.0006523635,0.0001078156,0.0002054158,0.000001538809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001237168,"about_ca_system_score_gemma":0.00006353119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001466105,"about_ca_topic_score_gemma":0.00003104982,"domain_scores_codex":[0.9991253,0.00003845417,0.0002081832,0.0001112544,0.0003159181,0.0002008984],"domain_scores_gemma":[0.9994081,0.00005880651,0.0001482669,0.000133286,0.0001337917,0.0001177901],"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":[0.00001860783,0.00003886579,0.005240593,0.00003185359,0.00002775646,0.001642499,0.007876969,0.9329338,0.05064973,0.000005990842,0.00000343109,0.001529877],"study_design_scores_gemma":[0.001982904,0.0005290561,0.001641852,0.0003123396,0.0001089142,0.007309244,0.0627326,0.9205059,0.004299785,0.000008333578,0.0002672546,0.0003017573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838906,0.00002301134,0.01419045,0.000003025352,0.0006361013,0.00005463027,0.000001062999,0.00001329161,0.001187808],"genre_scores_gemma":[0.9883016,0.000005031357,0.01158382,0.000002884404,0.00003662448,1.257714e-9,3.073837e-8,0.00000648027,0.00006357518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05485563,"threshold_uncertainty_score":0.3723662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03493014996335591,"score_gpt":0.2570845628898798,"score_spread":0.2221544129265239,"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."}}