{"id":"W3195448459","doi":"10.1111/1755-0998.13485","title":"Towards reproducible metabarcoding data: Lessons from an international cross‐laboratory experiment","year":2021,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; McGill University; Fisheries and Oceans Canada","funders":"","keywords":"Biology; Comparability; Sample (material); Protocol (science); Raw data; Workflow; Computational biology; DNA extraction; Consistency (knowledge bases); Data mining; Data science; Computer science; Polymerase chain reaction; Database; Genetics; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3227092,0.00199926,0.00219117,0.001717135,0.002915608,0.007438519,0.005874532,0.004181623,0.001738576],"category_scores_gemma":[0.2710762,0.001086138,0.001628746,0.002750535,0.008471361,0.005702024,0.008764362,0.006861373,0.00111483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002620631,"about_ca_system_score_gemma":0.008073818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003563938,"about_ca_topic_score_gemma":0.005048726,"domain_scores_codex":[0.8016192,0.1377961,0.01366026,0.01977547,0.02431867,0.002830174],"domain_scores_gemma":[0.6530936,0.1199617,0.01708822,0.09642297,0.1053197,0.008113739],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004940217,0.004861472,0.09482783,0.005259611,0.003931906,0.001625402,0.03766755,0.01197717,0.2378566,0.03558378,0.0260365,0.535432],"study_design_scores_gemma":[0.001915412,0.0285624,0.174601,0.006234957,0.003405184,0.00437911,0.01575113,0.02165143,0.2858049,0.1054849,0.3506591,0.001550441],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2493747,0.00772986,0.6803748,0.03672402,0.00362513,0.006198381,0.002343407,0.001633843,0.01199588],"genre_scores_gemma":[0.2517217,0.002477261,0.7279324,0.006763345,0.0007178263,0.003701065,0.003485073,0.001195329,0.002005948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6772908,"threshold_uncertainty_score":0.8352202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03425976845697633,"score_gpt":0.3102811491807375,"score_spread":0.2760213807237611,"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."}}