{"id":"W1969888541","doi":"10.1111/j.1755-0998.2009.02629.x","title":"The front‐end logistics of DNA barcoding: challenges and prospects","year":2009,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Canada; Ontario Genomics; Ontario Innovation Trust; Ontario Genomics Institute; Gordon and Betty Moore Foundation","keywords":"DNA barcoding; Bottleneck; Biology; Interfacing; Task (project management); Barcode; Data science; Computational biology; Evolutionary biology; Computer science; Engineering; Systems engineering; Operations management","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":[0.0001562263,0.0001122422,0.0001426062,0.00001478418,0.0002425275,0.000009319109,0.0002270648,0.00006798071,0.00007179436],"category_scores_gemma":[0.00006574175,0.00008505166,0.00003328411,0.00002877144,0.001055033,0.00002797913,0.0002966733,0.00008281937,0.00005335406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004717238,"about_ca_system_score_gemma":8.116688e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000853046,"about_ca_topic_score_gemma":0.00005872033,"domain_scores_codex":[0.9991879,0.00006050861,0.0001217432,0.0002419556,0.0001614799,0.0002264269],"domain_scores_gemma":[0.9996005,0.00008449753,0.00007700296,0.0001890645,0.000002593167,0.00004630713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009477006,0.0003477455,0.8781372,0.00002560478,0.0001511055,0.0001683925,0.006418006,0.0003687069,0.06767787,0.002862985,0.002104341,0.04164327],"study_design_scores_gemma":[0.0001848542,0.000298813,0.9819808,0.00000337677,0.00002439966,0.000007100072,0.0005012503,0.0000199633,0.005353398,0.002073534,0.009446627,0.0001058643],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804075,0.003831293,0.00001366301,0.002053072,0.00003308607,0.000157663,0.000002969029,0.00001645738,0.01348425],"genre_scores_gemma":[0.997305,0.001511156,0.0006970641,0.000245031,0.000007546539,0.000003691652,6.779635e-7,0.00000419626,0.0002256647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1038436,"threshold_uncertainty_score":0.3887314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254208702632552,"score_gpt":0.2013290573919939,"score_spread":0.1887869703656684,"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."}}