NAMERS: a purpose-built reference DNA sequence database to support applied eDNA metabarcoding
Bibliographic record
Abstract
Applied eDNA metabarcoding is increasingly being considered as a tool to inform management decisions, regulations, or policy development. Because these downstream considerations are coming to the forefront of eDNA applications, optimizing workflow elements is essential to increasing standardization, efficiency, and competency of metabarcoding results. Reference DNA sequences are critical workflow elements that currently lack consistent approaches to generating, curating, or publishing. We present a complete mitochondrial genome and nuclear ribosomal DNA cistron reference DNA sequence library for 92% of the freshwater fish species 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 for optimization of applied eDNA metabarcoding study design by analyzing the number of primer mismatches and species resolution power of existing metabarcoding markers. NAMERS demonstrates that high quality curated genomic information is within a reasonable reach to meet the increasing demand for actionable eDNA metabarcoding applications. The framework used here incorporating the pillars of accuracy, completeness and accessibility can be applied for new iterations of other reference sequence databases to bring DNA-based monitoring into a new era.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.025 |
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".