{"id":"W3004211383","doi":"10.1109/globalsip45357.2019.8969481","title":"Wide Separate 3D Convolution for Video Super Resolution","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Convolution (computer science); Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Motion estimation; Computation; Frame (networking); Image resolution; Motion compensation; Ground truth; Domain (mathematical analysis); Compensation (psychology); Algorithm; Artificial neural network; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002328644,0.00009743661,0.0001069113,0.00006509949,0.00007917912,0.00009292579,0.0004157235,0.00004215871,0.00001960695],"category_scores_gemma":[0.00008050118,0.00008842811,0.00003760498,0.0001740521,0.00002880374,0.001363613,0.0001306039,0.00006203405,0.0001556098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006978252,"about_ca_system_score_gemma":0.00005451178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009925817,"about_ca_topic_score_gemma":0.000002687285,"domain_scores_codex":[0.999112,0.00002020151,0.0001544303,0.0003455799,0.0001263878,0.0002414429],"domain_scores_gemma":[0.999229,0.00009136068,0.0000589911,0.0004224851,0.0001608378,0.00003728675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001463643,0.0001960054,0.00475665,0.0002703532,0.00003842873,0.000005690189,0.0006536753,0.0006047784,0.300573,0.5344536,0.09524325,0.06305829],"study_design_scores_gemma":[0.0003887244,0.0001466673,0.0003050026,0.0000321664,0.000003461302,0.000008599688,0.000006900692,0.8210472,0.03968099,0.06884897,0.0692821,0.0002492483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001478315,0.0001027652,0.9919651,0.0006068125,0.0001832808,0.0003529525,0.000001006395,0.0008322272,0.004477546],"genre_scores_gemma":[0.09637122,0.000004186191,0.899501,0.0009783481,0.00002247791,0.00004966815,0.000002868196,0.000009205583,0.003061008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8204424,"threshold_uncertainty_score":0.3605994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243963272279054,"score_gpt":0.2759416393883894,"score_spread":0.2635020066655989,"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."}}