{"id":"W4220787094","doi":"10.1002/essoar.10508849.2","title":"A deep learning approach to extract internal tides scattered by geostrophic turbulence","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"World Wide Web; Computer science","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":[0.0003695255,0.0007078559,0.0003404693,0.0003622629,0.000245626,0.000453235,0.0007914954,0.0006894082,0.0008823348],"category_scores_gemma":[0.001165134,0.0003667121,0.000492799,0.0004558009,0.0005904028,0.0006182662,0.001102962,0.001527373,0.0002648644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004959471,"about_ca_system_score_gemma":0.0006123593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00391174,"about_ca_topic_score_gemma":0.005451462,"domain_scores_codex":[0.9998806,0.00002007021,0.000006515035,0.00003673432,0.00003237727,0.00002364979],"domain_scores_gemma":[0.9997155,0.0001297652,0.00003945812,0.00003659184,0.00005395159,0.00002467884],"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":[0.00004077547,0.00005701211,0.001035813,0.00002917131,0.0000486854,0.00004896562,0.00002686052,0.9312501,0.005241815,0.005566242,0.001567804,0.05508671],"study_design_scores_gemma":[0.000001098445,0.000005350867,0.00008007022,0.000001453229,0.000001604452,0.000003979625,0.000001237882,0.9980834,0.0004817227,0.001205375,0.0001330727,0.000001750772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04808375,0.0003425136,0.9482271,0.0004372985,0.00007959903,0.00002820441,0.0002374791,0.0007013381,0.001862642],"genre_scores_gemma":[0.8153527,0.000381033,0.1758574,0.000306503,0.0001134729,0.00009324006,0.001256443,0.0001239469,0.006515391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00391174,"threshold_uncertainty_score":0.007777929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164234421070948,"score_gpt":0.2091691488491869,"score_spread":0.1975268046384774,"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."}}