{"id":"W4388399456","doi":"10.20944/preprints202311.0231.v1","title":"Single Image Super Resolution using Deep Residual Learning","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Residual; Artificial intelligence; Computer science; Deep learning; Convolution (computer science); Interpolation (computer graphics); Transpose; Sampling (signal processing); Image quality; Image (mathematics); Pattern recognition (psychology); Computer vision; Machine learning; Algorithm; Artificial neural network; Filter (signal processing)","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":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001505479,0.0005673392,0.0005493503,0.0004967468,0.0004489014,0.0003265667,0.002804313,0.0004786917,0.00005255988],"category_scores_gemma":[0.001650795,0.0006677144,0.0002038033,0.0005892532,0.0002303393,0.001209405,0.01282689,0.002097092,0.0008099462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006048171,"about_ca_system_score_gemma":0.0003013709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001862079,"about_ca_topic_score_gemma":0.0000127918,"domain_scores_codex":[0.9951046,0.0003969716,0.0007634176,0.002159667,0.0007483276,0.0008270745],"domain_scores_gemma":[0.9961986,0.0001645215,0.0005657625,0.002425111,0.0004756703,0.0001703338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005633472,0.0003494525,0.06261597,0.0007788566,0.00015431,0.000325428,0.004845828,0.01832644,0.9024904,0.001597634,0.000226838,0.008232542],"study_design_scores_gemma":[0.0004656523,0.00007791214,0.02298704,0.001492646,0.00009120387,0.00009932438,0.0001598728,0.4811065,0.3872082,0.1011727,0.002879323,0.002259604],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1018485,0.0002144975,0.8894557,0.0005729201,0.0006974897,0.0004714543,0.000003563549,0.004970902,0.001765006],"genre_scores_gemma":[0.351262,0.00007652264,0.6469349,0.00007868111,0.0002845023,0.0001028095,0.00002760278,0.0001298611,0.001103143],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5152822,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2000484686478735,"score_gpt":0.3832797132569491,"score_spread":0.1832312446090756,"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."}}