{"id":"W4414525253","doi":"10.3389/fmars.2025.1701125","title":"Editorial: Remote sensing applications in oceanography with deep learning","year":2025,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Deep learning; Synthetic aperture radar; Identification (biology); Shore; Mesoscale meteorology; Radar; Interoperability; Satellite; Automation; Lidar","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":[0.005705366,0.00335287,0.00417068,0.003897913,0.002068783,0.0087899,0.004759277,0.01650885,0.03141885],"category_scores_gemma":[0.02104788,0.00130181,0.003237985,0.002037328,0.003389795,0.005697835,0.001856731,0.02048693,0.02624871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002202806,"about_ca_system_score_gemma":0.002011043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001426509,"about_ca_topic_score_gemma":0.002507816,"domain_scores_codex":[0.9960419,0.0005433656,0.0005544312,0.0007204995,0.00183384,0.0003060905],"domain_scores_gemma":[0.9764654,0.01153331,0.00122694,0.0007326738,0.0072416,0.002800206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003668315,0.00001028811,0.00002440518,0.0001931768,0.00001629586,0.00004822783,0.000006043016,0.00003428568,0.00004987942,0.0003008777,0.9936876,0.005592308],"study_design_scores_gemma":[0.00006387864,0.00003371134,0.0002979691,0.0004229501,0.00003300512,0.0001785561,0.00001822779,0.0002286716,0.0001095598,0.001200408,0.9973903,0.00002287101],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00003576148,0.006193222,0.0003628821,0.03829253,0.9532251,0.00002119125,0.0001587585,0.0001485536,0.001561965],"genre_scores_gemma":[0.0002979371,0.003833906,0.0001320021,0.01671305,0.9736156,0.00002252718,0.00005837981,0.00006100358,0.005265625],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03141885,"threshold_uncertainty_score":0.1051065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003863729967630197,"score_gpt":0.2498032304057656,"score_spread":0.2459395004381354,"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."}}