{"id":"W2142741856","doi":"10.1109/36.964983","title":"Errors in bathymetric retrievals using linear dispersion in 3-D FFT analysis of marine radar ocean wave imagery","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Canada Excellence Research Chairs, Government of Canada","keywords":"Bathymetry; Significant wave height; Wind wave; Wave height; Geology; Surface wave; Wave shoaling; Airy wave theory; Wavelength; Radar; Dispersion (optics); Shoaling and schooling; Geodesy; Radar imaging; Remote sensing; Wave propagation; Optics; Breaking wave; Stokes wave; Physics; Computer science; Longitudinal wave; Mechanical wave; Oceanography; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006669571,0.0002012213,0.0004347336,0.002315853,0.0002384076,0.00004304999,0.00008382569,0.0001180108,0.00005249194],"category_scores_gemma":[0.00002807788,0.0001691912,0.0001481347,0.006486961,0.0002673955,0.0002848243,0.000002665018,0.0003063798,0.000002126694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002886931,"about_ca_system_score_gemma":0.00005613957,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01547391,"about_ca_topic_score_gemma":0.004437657,"domain_scores_codex":[0.9980772,0.0001189252,0.0004830794,0.0004870142,0.0003884589,0.0004453018],"domain_scores_gemma":[0.9992657,0.0001940797,0.0001326033,0.0002253334,0.0000492156,0.000133102],"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":[0.0001526849,0.00004967078,0.004304623,0.00002497471,0.00005053618,0.0002700353,0.0006514706,0.08828864,0.002416454,2.493478e-7,8.028119e-7,0.9037899],"study_design_scores_gemma":[0.0002720423,0.00007292975,0.07076214,0.000105272,0.0001110578,0.00008647315,0.0005137584,0.9268119,0.001025043,0.00003048116,0.00001374135,0.0001951488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962665,0.00009543768,0.03639217,0.00009946179,0.0003137093,0.0001252889,0.00001354143,0.00001669586,0.0002787342],"genre_scores_gemma":[0.9596064,0.0007382747,0.03943683,0.00007708886,0.00002317796,1.644154e-10,0.000006734033,0.000006048051,0.0001054546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9035947,"threshold_uncertainty_score":0.9910821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0215040362266517,"score_gpt":0.238391857503912,"score_spread":0.2168878212772603,"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."}}