Maximizing the reliability and the number of species assignments in metabarcoding studies
Bibliographic record
Abstract
Abstract The use of environmental DNA (eDNA) for biodiversity assessments has increased rapidly over the last decade. However, the reliability of taxonomic assignments in metabarcoding studies is variable, and affected by the reference databases and the assignment methods used. Species level assignments are usually considered as reliable using regional libraries but unreliable using public repositories. In this study, we aimed to test this assumption for metazoan species detected in the Gulf of St. Lawrence, in the Northwest Atlantic. We first created a regional library with COI barcode sequences including a reliability ranking system for species assignments. We then estimated the accuracy of the public repository NCBI-nt for species assignments using sequences from the regional library, and contrasted assigned species and their reliability using NCBI-nt or the regional library with a metabarcoding dataset and popular assignment methods. With NCBI-nt and sequences from the regional library, Blast-LCA was the most accurate method for species assignments but the proportions of accurate species assignments were higher with Blast-TopHit (>80 % overall taxa, between 70 and 90 % amongst taxonomic groups). With the metabarcoding dataset, the reliability of species assignments was greater using the GSL-rl compared to NCBI-nt. However, we also observed that the total number of reliable species assignments could be maximized using both GSL-rl and NCBI-nt, and their optimal assignment methods, which differed. The use of a two-step approach in species assignments, using a regional library and a public repository, could improve the reliability and the number of detected species in metabarcoding studies.
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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 | 0.040 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".