{"id":"W2744782154","doi":"10.1109/lgrs.2017.2733538","title":"Estimation of Significant Wave Height From X-Band Marine Radar Images Based on Ensemble Empirical Mode Decomposition","year":2017,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Buoy; Hilbert–Huang transform; Amplitude; Remote sensing; Synthetic aperture radar; Radar; Radar imaging; Significant wave height; Mode (computer interface); Normalization (sociology); Mean squared error; Wind wave; Computer science; Geology; Mathematics; Physics; Statistics; White noise; Telecommunications; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002452989,0.0001903063,0.0002474128,0.0001177922,0.0007552532,0.0002421683,0.0001384434,0.00007440743,0.00001219748],"category_scores_gemma":[0.00004991132,0.0001452615,0.00006983293,0.00008314085,0.0005412178,0.0002547949,0.00001223283,0.0001572737,0.000008281535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009284656,"about_ca_system_score_gemma":0.00003782449,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008875404,"about_ca_topic_score_gemma":0.0002424846,"domain_scores_codex":[0.9985216,0.00007646839,0.0002501211,0.0004503,0.0003750352,0.000326446],"domain_scores_gemma":[0.999016,0.0001785233,0.0002361772,0.0004009401,0.00003235999,0.0001359623],"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":[0.0001127711,0.00001318308,0.001147601,0.00002185271,0.00001039989,0.0001377806,0.0001871444,0.01861421,0.09552461,4.368092e-7,0.000260408,0.8839696],"study_design_scores_gemma":[0.0002924499,0.00008440508,0.08571298,0.0001263996,0.00002086609,0.00002187619,0.00002090908,0.8634109,0.04975627,0.0003391261,0.00002746011,0.0001863297],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590164,0.00001325565,0.03565954,0.003526373,0.0005982132,0.0001166984,0.00003147782,0.00002441987,0.001013601],"genre_scores_gemma":[0.9226938,0.00002448509,0.07586309,0.001212797,0.0001300829,6.651387e-10,0.00004180942,0.000005448662,0.00002842524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8837833,"threshold_uncertainty_score":0.9977246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01883750476223049,"score_gpt":0.2774184962541557,"score_spread":0.2585809914919252,"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."}}