{"id":"W3087750740","doi":"10.1007/978-3-030-67070-2_1","title":"AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Social Innovation; McMaster University","funders":"","keywords":"Computer science; Image (mathematics); Focus (optics); Resolution (logic); Set (abstract data type); FLOPS; Magnification; Artificial intelligence; Factor (programming language); Superresolution; State (computer science); Algorithm; Parallel computing","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":[0.007614141,0.002326352,0.002239178,0.001476139,0.000970754,0.004156718,0.003478353,0.00546902,0.01826541],"category_scores_gemma":[0.01590293,0.001140852,0.001293349,0.00156218,0.002022747,0.004644645,0.006033997,0.00674504,0.0137565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001338659,"about_ca_system_score_gemma":0.003134338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002275223,"about_ca_topic_score_gemma":0.001593425,"domain_scores_codex":[0.9963211,0.0008535921,0.0001377951,0.0004389951,0.001949872,0.0002986875],"domain_scores_gemma":[0.9895689,0.004610905,0.0002609221,0.00159695,0.003070161,0.0008921964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001012514,0.0002354288,0.0003412387,0.002571364,0.0002959739,0.0002425655,0.0001906453,0.02848002,0.02529123,0.2952936,0.2717288,0.3743166],"study_design_scores_gemma":[0.0001584123,0.0002889932,0.001039924,0.000459224,0.0001408692,0.0008192484,0.0001125971,0.2786509,0.03475405,0.4338462,0.2496111,0.0001183941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003543156,0.01415225,0.9353271,0.01250112,0.002472092,0.0002272121,0.00221291,0.002213648,0.02735052],"genre_scores_gemma":[0.05642092,0.01544915,0.8472341,0.005922046,0.005257922,0.0008151908,0.01058273,0.002266279,0.05605169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01826541,"threshold_uncertainty_score":0.06110382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0374974150302877,"score_gpt":0.3556962747484532,"score_spread":0.3181988597181655,"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."}}