{"id":"W2794428059","doi":"10.1111/eva.12604","title":"Optimization and performance testing of a sequence processing pipeline applied to detection of nonindigenous species","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Biology; Pipeline (software); Sequence (biology); Ecology; Evolutionary biology; Computer science; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007320724,0.00007354896,0.00008266333,0.00003944587,0.0003695909,0.000003443626,0.000103565,0.00002771984,0.00005721621],"category_scores_gemma":[0.00001279408,0.00007944657,0.000008373888,0.0004145391,0.000541652,0.0001154186,0.0001658348,0.0000349221,0.0000476666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001187671,"about_ca_system_score_gemma":0.000005222802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005050621,"about_ca_topic_score_gemma":0.000008019015,"domain_scores_codex":[0.9993622,0.000006783086,0.0001640207,0.0001993797,0.0001600571,0.0001075703],"domain_scores_gemma":[0.9996768,0.00002326894,0.0001141016,0.0001253813,0.0000251461,0.00003530704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003321475,0.0001520443,0.4144444,0.00005831838,0.000007661057,8.556201e-8,0.001217587,0.0430823,0.507546,0.00003744595,0.0000823179,0.03333857],"study_design_scores_gemma":[0.0001187328,0.000198452,0.9292897,0.00001946603,0.00002093223,0.000006643063,0.000269755,0.02031747,0.04850944,0.00005868531,0.001044863,0.0001458323],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8566169,0.00004609935,0.1375662,0.00004423398,0.0000116099,0.0005420648,0.00002868544,0.00003330119,0.005110975],"genre_scores_gemma":[0.8740463,0.00002100849,0.1257351,0.00002019417,0.00002378211,0.0000542072,0.000006063532,0.000004238452,0.00008903714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5148453,"threshold_uncertainty_score":0.3239737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902707375063873,"score_gpt":0.2116915871504146,"score_spread":0.1926645133997759,"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."}}