{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00173925,0.0002199314,0.0004918227,0.000831881,0.0009252155,0.0007714397,0.00169974,0.0003611416,0.00001305064],"category_scores_gemma":[0.0002247024,0.0002527983,0.00009939917,0.0005961032,0.0003615748,0.004188735,0.007114585,0.0006207282,0.00002565184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002986925,"about_ca_system_score_gemma":0.0009207527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002000893,"about_ca_topic_score_gemma":0.00003675675,"domain_scores_codex":[0.9969717,0.0003750433,0.0004394743,0.0004568606,0.001360589,0.0003963158],"domain_scores_gemma":[0.9967632,0.0001922379,0.0004561742,0.001136208,0.001193312,0.0002588761],"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":[0.0006157809,0.0003698088,0.002196624,0.02471768,0.0008469265,0.001141674,0.1157539,0.02211953,0.003684345,0.2322146,0.03352547,0.5628138],"study_design_scores_gemma":[0.002223411,0.0001332785,0.003707723,0.006496774,0.00003496143,0.00009488935,0.02143677,0.91674,0.000628055,0.0002199216,0.04750306,0.0007811884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03090827,0.0002382336,0.9549515,0.0003240469,0.000695303,0.0005341718,0.00008723757,0.0001661913,0.01209503],"genre_scores_gemma":[0.9559628,0.0001025043,0.04359157,0.00002062555,0.00007548669,0.000001836476,0.0001888798,0.000005782903,0.00005046644],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9250546,"threshold_uncertainty_score":0.9999924,"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."}}