{"id":"W1986302430","doi":"10.1371/journal.pone.0126409","title":"DIDA: Distributed Indexing Dispatched Alignment","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; BC Cancer Agency; University of British Columbia","funders":"National Human Genome Research Institute; BC Cancer Agency; University of British Columbia; Genome British Columbia; Canada's Michael Smith Genome Sciences Centre; Genome Canada","keywords":"Computer science; Scalability; Search engine indexing; Workflow; Software; Multiple sequence alignment; Sequence alignment; Data mining; Substring; Modular design; Preprocessor; Information retrieval; Artificial intelligence; Database; Programming language; Data structure; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002777445,0.002295095,0.002299505,0.001754209,0.00234743,0.003642889,0.006263396,0.002057135,0.01926254],"category_scores_gemma":[0.009297649,0.001521176,0.002222108,0.002478014,0.001465268,0.003388521,0.006555508,0.004402894,0.01537407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001889355,"about_ca_system_score_gemma":0.003432986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004210663,"about_ca_topic_score_gemma":0.004613567,"domain_scores_codex":[0.9972088,0.0005124857,0.0002601866,0.001052441,0.0006213349,0.0003447416],"domain_scores_gemma":[0.9968421,0.001068615,0.0002177365,0.001043847,0.0004562476,0.000371519],"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":[0.002009874,0.0005526156,0.002920136,0.00180694,0.0003528603,0.0006753324,0.0007771738,0.107929,0.0322292,0.08981874,0.3073558,0.4535722],"study_design_scores_gemma":[0.000845725,0.0002236334,0.0006475864,0.0000962531,0.0000702065,0.0005083875,0.0001821735,0.6920434,0.01762504,0.1397417,0.147824,0.0001919406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003256544,0.0005348136,0.927412,0.000320029,0.0005991551,0.0003125251,0.00241539,0.06151382,0.003635621],"genre_scores_gemma":[0.05738959,0.0004441513,0.9181319,0.0004369841,0.0002262891,0.001143625,0.009555047,0.007391929,0.005280358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01926254,"threshold_uncertainty_score":0.06443965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07898714744398383,"score_gpt":0.2391440644618991,"score_spread":0.1601569170179153,"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."}}