{"id":"W3183156840","doi":"10.4230/lipics.wabi.2021.13","title":"Compressing and indexing aligned readsets","year":2021,"lang":"en","type":"preprint","venue":"CINECA IRIS Institutial research information system (University of Pisa)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Search engine indexing; Information retrieval","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":[0.001198087,0.001337192,0.001449432,0.0044668,0.0009121604,0.00271437,0.00204918,0.0009910597,0.007565704],"category_scores_gemma":[0.01137883,0.0008021127,0.001384669,0.01095208,0.0006795404,0.004107652,0.002665432,0.002041449,0.007679445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006128568,"about_ca_system_score_gemma":0.001739585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175044,"about_ca_topic_score_gemma":0.001880436,"domain_scores_codex":[0.9970453,0.0002910892,0.0005942202,0.0005757858,0.001263129,0.0002305038],"domain_scores_gemma":[0.9951746,0.001177696,0.0003710854,0.001375863,0.001745427,0.0001553331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001477745,0.0002304857,0.002843304,0.002314713,0.0002055538,0.001276221,0.001089622,0.0279927,0.09954765,0.03004534,0.05916983,0.7738069],"study_design_scores_gemma":[0.0003659336,0.0008565743,0.006361808,0.0007556013,0.0002786393,0.00281061,0.002060714,0.2590446,0.2814699,0.1232523,0.3223023,0.0004410438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08788308,0.005242211,0.8130474,0.001163106,0.002971626,0.001077959,0.04488316,0.03086837,0.01286293],"genre_scores_gemma":[0.1177861,0.00301989,0.7718911,0.0004883703,0.0006966468,0.001002108,0.08962499,0.004580968,0.01090973],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007565704,"threshold_uncertainty_score":0.0253098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0704651181496647,"score_gpt":0.2999295147844821,"score_spread":0.2294643966348174,"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."}}