{"id":"W2954819764","doi":"10.3390/rs11131591","title":"Underwater Image Restoration Based on a Parallel Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Underwater; Computer science; Convolutional neural network; Artificial intelligence; Visibility; Computer vision; Image restoration; Image (mathematics); Transmission (telecommunications); Pattern recognition (psychology); Image processing; Telecommunications; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002770648,0.0005382869,0.0003836149,0.0003597921,0.0002169795,0.0003028053,0.0006919562,0.0004956099,0.001039847],"category_scores_gemma":[0.0005061441,0.0002880237,0.0003907076,0.0003034779,0.0003419435,0.0006391643,0.0005253524,0.0006470843,0.0002482041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004395068,"about_ca_system_score_gemma":0.000504776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006774219,"about_ca_topic_score_gemma":0.007946256,"domain_scores_codex":[0.9998705,0.00001372946,0.000005091978,0.00003742055,0.00005524979,0.00001784114],"domain_scores_gemma":[0.9998652,0.00003260768,0.00002069451,0.00002218994,0.00005046362,0.000008799352],"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":[0.000174413,0.00009572469,0.001141198,0.0001024365,0.00009466983,0.0001829123,0.00005434659,0.6369608,0.05913641,0.003046415,0.001919758,0.2970909],"study_design_scores_gemma":[0.000002350118,0.0000166922,0.0001294178,0.000002316307,0.000008822592,0.00002509335,0.000002527985,0.9947213,0.004371363,0.0003161241,0.0004000551,0.000003929226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03821371,0.0004085264,0.9575813,0.0001588727,0.00006185557,0.0000349404,0.00004138633,0.00105622,0.002443114],"genre_scores_gemma":[0.6677498,0.0006673617,0.3238075,0.0001721508,0.00005801244,0.00006959507,0.0002063395,0.00009834998,0.007170867],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006774219,"threshold_uncertainty_score":0.01346958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01449601271746775,"score_gpt":0.2458978699922649,"score_spread":0.2314018572747971,"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."}}