{"id":"W2330958039","doi":"10.1145/2925426.2926294","title":"Proteus","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Memory footprint; Implementation; Representation (politics); Flexibility (engineering); Chip; Proteus; Computer engineering; Layer (electronics); Parallel computing; Computer architecture; Computer hardware; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00001877915,0.0000261613,0.00002144914,0.00001010162,0.00002684529,0.000009425085,0.0003242008,0.000007606817,0.00002600845],"category_scores_gemma":[0.000006603433,0.00001358731,0.000009203619,0.0001101851,0.00001123055,0.0002043857,0.00008431082,0.00001088181,0.0004924458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007047252,"about_ca_system_score_gemma":0.000005314868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.991679e-7,"about_ca_topic_score_gemma":6.513519e-7,"domain_scores_codex":[0.9997021,0.000004389433,0.00004109595,0.0001207105,0.00004779484,0.00008389886],"domain_scores_gemma":[0.9996064,0.00003414687,0.00001165108,0.0003052939,0.00001345641,0.00002908369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[1.537606e-7,0.00000416317,0.00005062677,1.438037e-7,4.105819e-7,4.202709e-7,0.00000308593,0.000001679731,0.008167587,0.6783297,0.002296549,0.3111455],"study_design_scores_gemma":[0.0003122287,0.0000441573,0.002534682,0.000009122055,6.761802e-7,0.00001960005,8.645573e-7,0.0034362,0.09392829,0.4184802,0.4810059,0.000228075],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004013503,0.00000521455,0.9763548,0.00902234,0.00002763714,0.00008561096,9.315426e-8,0.0002369976,0.01386595],"genre_scores_gemma":[0.6292471,0.000007103276,0.36095,0.0006092223,0.00004473142,0.00007688026,3.975736e-8,0.000003146173,0.009061821],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6288457,"threshold_uncertainty_score":0.6329558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680896953813908,"score_gpt":0.2551322779741452,"score_spread":0.2383233084360061,"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."}}