{"id":"W2979888527","doi":"10.1007/978-3-030-32686-9_3","title":"Rpair: Rescaling RePair with Rsync","year":2019,"lang":"en","type":"book-chapter","venue":"CINECA IRIS Institutial research information system (University of Pisa)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Parsing; Hash function; Leverage (statistics); Computation; Data compression; Algorithm; Scheme (mathematics); Piecewise; Compression (physics); Theoretical computer science; Artificial intelligence; Programming language","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001567938,0.0002809079,0.0005321963,0.001099123,0.0006769861,0.0001719014,0.001901522,0.0003917304,0.00007253097],"category_scores_gemma":[0.00006649886,0.0002741552,0.0001970086,0.0003204858,0.0003829044,0.00398789,0.001429246,0.0007594556,0.001057779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004866003,"about_ca_system_score_gemma":0.001274849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004408873,"about_ca_topic_score_gemma":0.00004179835,"domain_scores_codex":[0.9965684,0.00009500242,0.0004512827,0.0004491011,0.00201415,0.0004220946],"domain_scores_gemma":[0.9961721,0.0001520842,0.0005134753,0.001557602,0.001359392,0.0002453098],"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":[0.0002764379,0.00001927831,0.00003561002,0.001690013,0.0001672894,0.0001404048,0.001826097,0.0007448004,0.0000100607,0.9350637,0.04007673,0.01994955],"study_design_scores_gemma":[0.001245621,0.0003843623,0.0001026007,0.00262704,0.00002508566,0.00006065783,0.001153948,0.07292568,0.00001293971,0.0001403864,0.9208906,0.0004310929],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00008541804,0.00007023732,0.461059,0.0001816511,0.0003715483,0.000728771,0.000123773,0.0003322239,0.5370473],"genre_scores_gemma":[0.2727606,0.0007583909,0.2562466,0.0002742213,0.00135077,0.000009866038,0.00159314,0.0001684611,0.4668379],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9349234,"threshold_uncertainty_score":0.9999711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04805417272412938,"score_gpt":0.25484428432126,"score_spread":0.2067901115971306,"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."}}