{"id":"W2603763990","doi":"10.1109/tgrs.2017.2659538","title":"A Combined Prognostic Model Based on Machine Learning for Tidal Current Prediction","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Robustness (evolution); Computer science; Univariate; Wavelet transform; Wavelet; Machine learning; Algorithm; Artificial intelligence; Data mining; Multivariate statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001574281,0.0001487079,0.0001214219,0.0001155829,0.001147487,0.0001277754,0.00007709082,0.00005536957,9.304229e-7],"category_scores_gemma":[0.00002835939,0.0001366981,0.00005769898,0.00006421111,0.00008359285,0.0001475925,0.000001008072,0.0002585507,0.000001460277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003038229,"about_ca_system_score_gemma":0.00002582626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002925197,"about_ca_topic_score_gemma":0.00003841372,"domain_scores_codex":[0.999218,0.00001260642,0.0001373228,0.0002296733,0.0001513129,0.0002510804],"domain_scores_gemma":[0.9995657,0.00009684734,0.00004189834,0.0001808353,0.00003172935,0.00008294297],"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":[0.0000207487,0.00001131583,0.000006556832,0.0000349737,0.000003363225,8.100921e-7,0.00006725896,0.6084624,0.00111136,0.000003561796,0.000002053227,0.3902756],"study_design_scores_gemma":[0.0004659717,0.0001538721,0.0001275043,0.0003314056,0.00002342781,0.000003952563,0.000008458945,0.9949324,0.003601512,0.00006815623,0.0001414476,0.0001418804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0722377,0.00001795507,0.9258011,0.00006395301,0.001159446,0.0001496392,0.00001612689,0.0001794477,0.0003746088],"genre_scores_gemma":[0.9866869,0.00005516042,0.01304152,0.0000180635,0.00004931529,6.891938e-7,0.000003567913,0.00002151506,0.0001232638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9144492,"threshold_uncertainty_score":0.8825654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027205879562888,"score_gpt":0.2395697800418151,"score_spread":0.2192977212461862,"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."}}