{"id":"W2806728537","doi":"10.1080/1755876x.2018.1479571","title":"On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal","year":2018,"lang":"en","type":"article","venue":"Journal of Operational Oceanography","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Science and Engineering Research Board; National Oceanic and Atmospheric Administration; Ministry of Earth Sciences; Indian National Centre for Ocean Information Services; Indian Space Research Organisation","keywords":"Geology; Bathymetry; Bay; Submarine pipeline; Oceanography; Tide gauge; Current (fluid); Zonal and meridional; Waves and shallow water; Sea level","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001587479,0.0003545639,0.0001838876,0.001307552,0.0002888713,0.0005384329,0.0002746128,0.0001731965,0.000544122],"category_scores_gemma":[0.0004925653,0.00009013485,0.0001861666,0.001601425,0.0001340224,0.0001926431,0.0002812688,0.0001177256,0.0003254665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003062229,"about_ca_system_score_gemma":0.0003200675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05194909,"about_ca_topic_score_gemma":0.06375598,"domain_scores_codex":[0.9998627,0.00001172002,0.00001118868,0.0000270458,0.00004406291,0.00004329167],"domain_scores_gemma":[0.9998186,0.0000364737,0.00004419168,0.00001637585,0.00005794048,0.00002644444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002134213,0.0001523652,0.620028,0.0002032454,0.00008411054,0.002236291,0.001298311,0.008141102,0.130099,0.0001608316,0.001297023,0.2360862],"study_design_scores_gemma":[0.000006561636,0.00005364163,0.9812423,0.00000834183,0.00003620638,0.000214406,0.0007550596,0.0109215,0.005013743,0.00003143011,0.001700735,0.00001604182],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960088,0.0001594136,0.001954288,0.00003876263,0.000008383438,0.00001545363,0.0008334464,0.00006830109,0.000913226],"genre_scores_gemma":[0.9945478,0.0002221892,0.002780859,0.0000171271,0.00002189407,0.00001210277,0.001637411,0.00001081725,0.000749769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05194909,"threshold_uncertainty_score":0.1032934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855680922165206,"score_gpt":0.2402461579295442,"score_spread":0.2216893487078921,"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."}}