{"id":"W4414453679","doi":"10.1007/978-3-032-05228-5_6","title":"Prefix-Free Parsing for Merging Big BWTs","year":2025,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Parsing; Footprint; Memory footprint; Big data; Word (group theory)","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":[0.00213864,0.001405307,0.001593853,0.002878946,0.001769634,0.0034388,0.003097077,0.002252637,0.02127953],"category_scores_gemma":[0.01112726,0.001716657,0.001697624,0.005992792,0.001855713,0.008481305,0.005604466,0.002690074,0.00758881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007707,"about_ca_system_score_gemma":0.002244157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002485698,"about_ca_topic_score_gemma":0.004601342,"domain_scores_codex":[0.9971507,0.0005138124,0.0004157281,0.0006545731,0.0009031192,0.0003620031],"domain_scores_gemma":[0.990815,0.003696448,0.0003069558,0.003805595,0.001185125,0.0001907286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001421446,0.0002788119,0.002257753,0.001185207,0.0002464829,0.001325304,0.001352904,0.02905958,0.04563114,0.1529204,0.04831497,0.716006],"study_design_scores_gemma":[0.0001863789,0.0002878432,0.001229456,0.0003297848,0.0003209439,0.0009017316,0.0008483661,0.249357,0.1115074,0.5321681,0.1026292,0.0002337296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02339828,0.0007616358,0.9416217,0.0003807085,0.0003543216,0.0001960371,0.002239861,0.0214681,0.009579351],"genre_scores_gemma":[0.2054209,0.0004965876,0.7630963,0.0003984758,0.000180898,0.000296006,0.009658618,0.009053258,0.01139897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02127953,"threshold_uncertainty_score":0.07118708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648811670444234,"score_gpt":0.2741718686162525,"score_spread":0.2576837519118102,"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."}}