{"id":"W4311353305","doi":"10.1049/rsn2.12361","title":"Blind time‐domain motion compensation for synthetic Doppler spectra obtained from an HF‐radar on a floating platform","year":2022,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Doppler effect; Acoustics; Radar; Antenna (radio); Autocorrelation; Physics; Frequency domain; Motion compensation; Computer science; Mathematics; Telecommunications; Algorithm; Computer vision; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005803736,0.0002004487,0.000210589,0.0001005535,0.0009205433,0.0001080696,0.000150765,0.00007996151,0.0007093461],"category_scores_gemma":[0.00001830451,0.0001948023,0.00009810897,0.000234461,0.00004382359,0.0003322493,0.00001212228,0.0002506716,0.0000764821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006609469,"about_ca_system_score_gemma":0.0000531984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007640988,"about_ca_topic_score_gemma":0.0001582352,"domain_scores_codex":[0.9982135,0.000150396,0.0003400497,0.0004444095,0.00053168,0.0003200048],"domain_scores_gemma":[0.9991806,0.0002257574,0.0002223843,0.0002176729,0.00004356768,0.0001100739],"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":[0.002811814,0.0004282558,0.003638566,0.0001068793,0.0001851821,0.0001002912,0.01178488,0.1008344,0.02228379,0.001403421,0.001718554,0.854704],"study_design_scores_gemma":[0.002999599,0.001527577,0.03456021,0.0001218108,0.00009062707,0.0001004415,0.003640738,0.9213194,0.003610115,0.02436231,0.006897115,0.0007700194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947467,0.00005570201,0.002161223,0.0006529763,0.0004298402,0.0006639549,0.0004769136,0.0001023141,0.0007103456],"genre_scores_gemma":[0.964378,0.000002968602,0.0274437,0.0002002801,0.0004562992,2.37219e-7,0.007403331,0.00001767501,0.00009752674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.853934,"threshold_uncertainty_score":0.7943807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01816131189621885,"score_gpt":0.2322403312926545,"score_spread":0.2140790193964356,"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."}}