{"id":"W4401809337","doi":"10.1109/cvprw50498.2020.10645327","title":"Retraction Notice: FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object Recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Notice; Computer science; Cognitive neuroscience of visual object recognition; Object (grammar); Artificial intelligence; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002159449,0.0001237224,0.0001250546,0.00002416962,0.0002563731,0.0001241593,0.0002910299,0.00008672833,0.00005323298],"category_scores_gemma":[0.000039385,0.0001124867,0.0001106048,0.0005553613,0.00001906398,0.0001388031,0.00004636882,0.0001737658,0.00007142541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002175573,"about_ca_system_score_gemma":0.00006268748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005030133,"about_ca_topic_score_gemma":0.000009782086,"domain_scores_codex":[0.9988003,0.00003677876,0.0002271742,0.0004321635,0.0002168937,0.0002866796],"domain_scores_gemma":[0.9991462,0.0002322301,0.0001080374,0.0002483093,0.000114731,0.0001505452],"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":[0.0001508356,0.000284731,0.00006816709,0.0000617395,0.00003702147,0.000007672752,0.000170523,0.4173414,0.001414188,0.02375393,0.1287814,0.4279284],"study_design_scores_gemma":[0.0003927127,0.0001570876,0.0002786471,0.00001115261,0.00001355964,0.000002620775,0.000007988426,0.9830281,0.001443288,0.001545829,0.01294642,0.0001726637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008130773,0.0000246201,0.9768043,0.01248473,0.0004235147,0.0006178137,0.000009689236,0.0002952889,0.001209294],"genre_scores_gemma":[0.7302378,0.000003640589,0.2605695,0.007432908,0.001352188,0.0002147151,0.00005836695,0.00001745532,0.0001133553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7221071,"threshold_uncertainty_score":0.4587073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04616984897163188,"score_gpt":0.2601526964405105,"score_spread":0.2139828474688786,"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."}}