{"id":"W6929345185","doi":"10.48448/9pdg-b496","title":"Segatron: Segment-Aware Transformer for Language Modeling and Understanding","year":2021,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Perplexity; Language model; Transformer; Security token; Sentence; Question answering; Encoding (memory)","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":[0.000111136,0.0001654411,0.0001344068,0.00008466611,0.0001716323,0.00004397768,0.0001778775,0.0001491807,0.00004151712],"category_scores_gemma":[0.000007765145,0.0001620856,0.00004151234,0.0001277117,0.0002009738,0.000003742748,0.00004563739,0.00008634142,4.454551e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005530869,"about_ca_system_score_gemma":0.0001830994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002067231,"about_ca_topic_score_gemma":0.0001658667,"domain_scores_codex":[0.9989808,0.000005328711,0.0001184893,0.0004957903,0.0001024142,0.0002971475],"domain_scores_gemma":[0.9995956,0.000003977943,0.00005403973,0.0002424426,0.00003433416,0.00006953687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007797345,0.00002652284,0.000005451113,0.00007992691,0.00002596846,6.409318e-7,0.00003900915,0.0001357865,0.9848694,0.002817211,0.007686176,0.004306161],"study_design_scores_gemma":[0.001263717,0.0005008507,5.653293e-7,0.0003560628,0.0001166469,0.0000415597,0.002683664,0.04107092,0.4971313,0.003070464,0.452306,0.001458217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002756422,0.00249283,0.9858145,0.0001417259,0.00004100229,0.0003847032,0.00007859964,0.00004279161,0.01072825],"genre_scores_gemma":[0.3699786,0.006952631,0.3831515,0.001426535,0.001471828,0.0004894856,0.003449158,0.0009327906,0.2321475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6026629,"threshold_uncertainty_score":0.6609659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343247087210898,"score_gpt":0.3510913044773185,"score_spread":0.3276588336052095,"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."}}