{"id":"W3090465494","doi":"10.1007/978-3-030-59212-7_16","title":"Practical Random Access to SLP-Compressed Texts","year":2020,"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":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Grammar; Random access; Rule-based machine translation; Simple (philosophy); Encoding (memory); Compression (physics); Process (computing); Data compression; Theoretical computer science; Artificial intelligence; Programming language; Linguistics","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":[0.001655538,0.0008201012,0.001030569,0.001119019,0.0008137787,0.001960289,0.001113185,0.001292697,0.02505546],"category_scores_gemma":[0.01258753,0.0005616187,0.0005308141,0.002095438,0.001102406,0.003057085,0.003810582,0.00143332,0.007451408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005724811,"about_ca_system_score_gemma":0.00106891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007953027,"about_ca_topic_score_gemma":0.001522817,"domain_scores_codex":[0.9969378,0.0009903191,0.0001991077,0.0003407109,0.001161553,0.0003705945],"domain_scores_gemma":[0.9912627,0.005162403,0.0002078505,0.002470454,0.0007617751,0.0001348139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002117964,0.0002090723,0.0004483344,0.0006503898,0.00008011796,0.0006775399,0.0005061371,0.03456298,0.03998026,0.1287887,0.0379862,0.7539923],"study_design_scores_gemma":[0.0002975383,0.0003651138,0.0006485181,0.0001841055,0.00007008774,0.001334067,0.0004183955,0.6407321,0.08082218,0.2379404,0.03711717,0.00007035527],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06082557,0.001196165,0.8955265,0.001567452,0.0003547598,0.0003077651,0.00130574,0.005872837,0.03304323],"genre_scores_gemma":[0.4956794,0.001308119,0.4471643,0.0005696076,0.0008444173,0.0005811579,0.003977175,0.00119408,0.04868196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02505546,"threshold_uncertainty_score":0.08381891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1357245106991051,"score_gpt":0.3454069256700878,"score_spread":0.2096824149709827,"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."}}