{"id":"W2754332785","doi":"10.1049/iet-rsn.2017.0220","title":"Motion compensation for high‐frequency surface wave radar on a floating platform","year":2017,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Antenna (radio); Deconvolution; Computer science; Motion compensation; Compensation (psychology); Acoustics; Noise (video); Radar imaging; White noise; Remote sensing; Geology; Physics; Telecommunications; Artificial intelligence; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002949889,0.0004210855,0.0001891183,0.0001754238,0.0001647649,0.0002390522,0.0004248241,0.0003824694,0.0003455103],"category_scores_gemma":[0.0005977081,0.0001528294,0.0003449908,0.0001898432,0.0002667361,0.0004750111,0.000281485,0.0003658109,0.0001301728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002556011,"about_ca_system_score_gemma":0.0004799021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140289,"about_ca_topic_score_gemma":0.002512966,"domain_scores_codex":[0.9998623,0.00002393301,0.000004599874,0.00002654935,0.00006927673,0.00001330341],"domain_scores_gemma":[0.9998784,0.00003345993,0.00003477789,0.00001923662,0.00002917403,0.000004825814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001653076,0.00005650586,0.002365915,0.0001071122,0.00005153329,0.0001425051,0.000155979,0.565035,0.2724343,0.005019825,0.0003003738,0.1541657],"study_design_scores_gemma":[0.000005825478,0.00008219822,0.001483167,0.000005289943,0.000009217488,0.00007350682,0.00001654013,0.9621869,0.03507603,0.0003794855,0.0006666697,0.00001507831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1460053,0.000150361,0.852841,0.00004679283,0.0000287062,0.00001484949,0.00001270532,0.0001952541,0.0007050587],"genre_scores_gemma":[0.8427124,0.0001859088,0.1551622,0.00002063509,0.00001051438,0.00002149864,0.00006107629,0.00004160992,0.001784169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002140289,"threshold_uncertainty_score":0.004255652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03450147480178499,"score_gpt":0.2458669696047117,"score_spread":0.2113654948029267,"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."}}