{"id":"W2946589087","doi":"10.3390/rs11101206","title":"High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":211,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; National Natural Science Foundation of China; China National Space Administration","keywords":"Computer science; Convolutional neural network; Synthetic aperture radar; Artificial intelligence; Deep learning; Grid; Pattern recognition (psychology); Computer vision; Feature extraction; Focus (optics); Remote sensing; Geology","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":[0.0001851711,0.0001588263,0.0001723516,0.0001110161,0.0001274478,0.00006237843,0.0002253243,0.00006556258,0.000003302557],"category_scores_gemma":[0.00003082382,0.0001659122,0.00005640977,0.0007473969,0.00003458557,0.0002258793,0.00007745905,0.0002893993,0.0001016366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001474856,"about_ca_system_score_gemma":0.00003086782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004768122,"about_ca_topic_score_gemma":0.0000241601,"domain_scores_codex":[0.9985402,0.0001175399,0.000226905,0.0004809612,0.0002477384,0.0003866929],"domain_scores_gemma":[0.9989324,0.0003168618,0.0001084569,0.0005209406,0.00005519309,0.00006618375],"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":[0.00002787468,0.000009805692,0.00006225871,0.000004944472,0.000002264323,0.0000136068,0.00001390814,0.8595923,0.01288501,0.000620842,0.0001011075,0.1266661],"study_design_scores_gemma":[0.0003805611,0.00004914451,0.003947211,0.00005008978,0.000002071932,0.00002231821,0.000001848825,0.9882162,0.003755583,0.003021308,0.000381652,0.0001720501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2957218,0.00003362358,0.7009643,0.001376539,0.000932706,0.0003497101,0.000001193643,0.0002438666,0.0003762321],"genre_scores_gemma":[0.8901355,0.000002755592,0.1086356,0.0007967812,0.0003651435,3.037212e-8,0.000005446304,0.00001515063,0.00004361344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5944137,"threshold_uncertainty_score":0.6765704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443707138256823,"score_gpt":0.2401088034786429,"score_spread":0.2256717320960747,"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."}}