{"id":"W7100019555","doi":"","title":"Use of COADS Wind Data in Wave Hindcasting and Statistical Analysis","year":2015,"lang":"en","type":"article","venue":"","topic":"Plant Diversity and Evolution","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hindcast; Statistical analysis; Data set; Atmosphere (unit); Wind wave; Statistical model","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":[0.003219828,0.0006026403,0.000524875,0.002578654,0.0008268483,0.001272857,0.0007887817,0.0002659631,0.003822769],"category_scores_gemma":[0.01180859,0.0003542418,0.0004960509,0.003162266,0.0002514444,0.0006235502,0.0008226309,0.0007703853,0.001476665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004776255,"about_ca_system_score_gemma":0.001745386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03675972,"about_ca_topic_score_gemma":0.05193113,"domain_scores_codex":[0.9978679,0.0007707748,0.0002370122,0.0003174763,0.0006420811,0.0001646713],"domain_scores_gemma":[0.9937914,0.00182928,0.0004499763,0.001590029,0.002085475,0.0002538504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001382873,0.0005803172,0.3872305,0.0003446453,0.0005968554,0.0003140483,0.0006417385,0.1007937,0.01474818,0.00715989,0.05029775,0.4359097],"study_design_scores_gemma":[0.0004543455,0.000394961,0.4265332,0.0001056076,0.0003038589,0.0001823363,0.001027919,0.4127036,0.03430597,0.008058326,0.1157116,0.0002183132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5049946,0.0003058711,0.3704367,0.0005208643,0.0009487887,0.001401838,0.09412876,0.008515181,0.0187474],"genre_scores_gemma":[0.7018152,0.0001561923,0.2423505,0.00008847001,0.0001516292,0.001021198,0.04786893,0.001449017,0.005098957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03675972,"threshold_uncertainty_score":0.07309157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3064898674681114,"score_gpt":0.2575745732518749,"score_spread":0.04891529421623653,"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."}}