{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001686737,0.00009526553,0.0001442332,0.00002937835,0.00006483532,0.0001157984,0.0005910253,0.00003907028,0.000009016331],"category_scores_gemma":[0.00005731516,0.0000800585,0.0000208374,0.0001798597,0.00001725027,0.000418495,0.0006088814,0.00008737917,0.00009829257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006457543,"about_ca_system_score_gemma":0.00004237395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004197187,"about_ca_topic_score_gemma":0.000002070517,"domain_scores_codex":[0.9988332,0.00003428783,0.0001432836,0.0002652468,0.0005081369,0.0002158551],"domain_scores_gemma":[0.9990999,0.00002869659,0.00005696433,0.0005462269,0.00007352798,0.0001947226],"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":[0.000372459,0.04993115,0.1720374,0.0006736764,0.002626346,0.001027266,0.02162218,0.001605354,0.1486326,0.2146405,0.1503287,0.2365024],"study_design_scores_gemma":[0.003624283,0.0006221337,0.01905571,0.0006819721,0.0001029801,0.00001120018,0.0002324534,0.820945,0.1177036,0.02199721,0.01365815,0.001365282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0968061,0.0001634422,0.9004974,0.001219917,0.0001427162,0.0001664934,0.00003589989,0.0002567385,0.0007113377],"genre_scores_gemma":[0.8995852,0.00001191089,0.09967402,0.0002079931,0.0001509173,0.00002592839,0.00008591326,0.000009760072,0.0002483626],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8193397,"threshold_uncertainty_score":0.3264691,"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."}}