NAMERS: a purpose-built reference DNA sequence database to support applied eDNA metabarcoding
Bibliographic record
Abstract
ABSTRACT Applied eDNA metabarcoding is increasingly being used to generate actionable results to inform management decisions, regulations, or policy development. Because of these important downstream considerations, optimizing workflow elements is now essential to increasing standardization, efficiency, and confidence of metabarcoding results. Reference DNA sequences are critical workflow elements that currently lack consistent approaches to generating, curating, or publishing. Here we present a complete (mitochondrial genome and nuclear ribosomal DNA cistron) and high quality reference DNA sequence library for the freshwater fishes of British Columbia, Canada. This resource is published as the Novel Applied eDNA Metabarcoding Reference Sequences (NAMERS) repository ( https://namers.ca ), a user-friendly and interactive website for specialists and non-specialists alike to explore and generate custom reference libraries for taxa and genes of interest. We demonstrate the power of NAMERS to optimize applied eDNA metabarcoding workflows at the study design stage by analyzing the number of primer mismatches and resolution power of existing metabarcoding markers. To meet the increasing demand for actionable eDNA metabarcoding applications, NAMERS demonstrates that high quality curated genomic information is within a reasonable reach. It is timely to establish this framework as the new gold standard and coordinate our efforts to generate this type of reference data at scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.035 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".