{"id":"W4393040678","doi":"10.3390/ai5010021","title":"Single Image Super Resolution Using Deep Residual Learning","year":2024,"lang":"en","type":"article","venue":"AI","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Toronto Zoo","funders":"","keywords":"Residual; Artificial intelligence; Deep learning; Computer science; Computer vision; Image (mathematics); Pattern recognition (psychology); Algorithm","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":[0.0002146524,0.00009774901,0.00007848407,0.0001203337,0.0001623823,0.0005104474,0.0003336196,0.00004543853,0.000009478228],"category_scores_gemma":[0.0001140064,0.00009724284,0.00002986681,0.0003867628,0.00005766594,0.001852105,0.0002509097,0.0002596233,0.00004811183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013632,"about_ca_system_score_gemma":0.00005671432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001392247,"about_ca_topic_score_gemma":0.000001909475,"domain_scores_codex":[0.9990577,0.0000498962,0.0001315627,0.0003436656,0.0001770716,0.0002401667],"domain_scores_gemma":[0.9995732,0.00004889346,0.0000232631,0.0002457455,0.00007253921,0.00003632137],"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":[0.00000472303,0.00003929604,0.00008273923,0.00008628574,0.00001173896,0.0001795375,0.001618325,0.0003657851,0.837184,0.01287424,0.002498318,0.145055],"study_design_scores_gemma":[0.00005060133,0.00006657878,0.00002896715,0.0001527021,0.000006699119,0.00008158879,0.0000228482,0.8888968,0.06860144,0.02579852,0.01608509,0.0002081879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002448648,0.001237122,0.9923428,0.000922462,0.0001608402,0.00004809321,2.576395e-7,0.001599431,0.001240334],"genre_scores_gemma":[0.2064633,0.000007846575,0.7929442,0.0001948758,0.0001006344,0.000003734532,0.000001331666,0.00001776966,0.0002663015],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.888531,"threshold_uncertainty_score":0.4922256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02091539249879587,"score_gpt":0.304840264488246,"score_spread":0.2839248719894502,"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."}}