{"id":"W4400926086","doi":"10.1002/edn3.590","title":"Enhancing metabarcoding of freshwater biotic communities: A new online tool for primer selection and exploring data from 14 primer pairs","year":2024,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"3M (Canada); Parks Canada; York University; St. Lawrence River Institute of Environmental Sciences; McGill University; Queen's University","funders":"Queen's University","keywords":"Primer (cosmetics); Selection (genetic algorithm); Biology; Computational biology; Ecology; Computer science; Artificial intelligence; Chemistry","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002314588,0.0002867302,0.0002818881,0.00006026899,0.0002578023,0.00004498245,0.0004000275,0.00007534607,0.002276601],"category_scores_gemma":[0.00001702554,0.0002777587,0.00008053364,0.00009115563,0.0003439735,0.001036755,0.001572243,0.0001914445,0.0001957801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002762713,"about_ca_system_score_gemma":0.000003976318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001756092,"about_ca_topic_score_gemma":0.0005825139,"domain_scores_codex":[0.9983414,0.00006112039,0.0003436925,0.0005723718,0.0003479653,0.0003334937],"domain_scores_gemma":[0.9991171,0.000232552,0.00007822832,0.0004746628,7.63877e-7,0.00009668181],"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":[0.0000551685,0.0002387338,0.3905031,0.0000763123,0.0003357703,0.000004818276,0.004418083,0.00009864072,0.5939022,0.000008083836,0.002226688,0.008132413],"study_design_scores_gemma":[0.001423395,0.0003958787,0.5186945,0.0003378704,0.0007392424,0.00001452687,0.008272627,0.006354335,0.3967169,0.0002249675,0.06566016,0.001165567],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952902,0.001153424,0.001339569,0.0001085115,0.000223565,0.0004539932,0.001316359,0.00006624485,0.00004812485],"genre_scores_gemma":[0.9559185,0.0009499913,0.04171263,0.00008255328,0.00009832367,0.00001954972,0.0007031936,0.00004220402,0.0004730461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1971853,"threshold_uncertainty_score":0.9999675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06966734139842891,"score_gpt":0.2492416753491533,"score_spread":0.1795743339507244,"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."}}