{"id":"W2990503925","doi":"10.3390/rs11232826","title":"Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; McMaster University","keywords":"Clutter; Radar; Remote sensing; Constant false alarm rate; Computer science; Shore; Term (time); Amplitude; Geology; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005304906,0.0006808261,0.0005714282,0.0003892,0.0003387934,0.0005570414,0.0006388105,0.0006785596,0.0009511024],"category_scores_gemma":[0.001087644,0.0002956229,0.0004352613,0.0004281899,0.0002472317,0.0007799895,0.0003983051,0.0007538796,0.000287406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004661766,"about_ca_system_score_gemma":0.0006261524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01276574,"about_ca_topic_score_gemma":0.00844472,"domain_scores_codex":[0.9997602,0.00003323407,0.00001928811,0.0000702644,0.00007303144,0.00004389207],"domain_scores_gemma":[0.9996862,0.00009772187,0.00003259526,0.00001776917,0.0001507812,0.00001486663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004034195,0.0002936629,0.004754257,0.00009248222,0.0001265723,0.000140229,0.00006624008,0.6582748,0.01280449,0.0007830845,0.001672216,0.3205886],"study_design_scores_gemma":[0.000004298825,0.00002888385,0.0002593667,0.000002584963,0.000008328629,0.000005540665,0.000002453666,0.9986691,0.000866565,0.00009911127,0.00005025704,0.000003500407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2006211,0.001372611,0.7909455,0.0003690298,0.0002384477,0.00008109648,0.0001092066,0.001824044,0.004438947],"genre_scores_gemma":[0.9530094,0.0004115279,0.04305249,0.0001551486,0.00005130916,0.00007370987,0.0001749549,0.0000277863,0.003043692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01276574,"threshold_uncertainty_score":0.02538288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612640640351272,"score_gpt":0.22516953896606,"score_spread":0.2090431325625473,"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."}}