Identifying early life stages of Great Lakes fishes using a metabarcoding approach
Bibliographic record
Abstract
Detection of early life stages of fishes is important for understanding life history patterns and critical spawning habitats. When feasible, identifying early life stages of fishes using morphology requires taxonomic expertise and can be challenging, time consuming, and imprecise. In this study, we used DNA metabarcoding to identify egg and larval batch samples from two sites in the species-rich East Sydenham River, Ontario, Canada. We used a two-step PCR metabarcoding approach to amplify a highly variable region of the mitochondrial COI gene from 1075 mixed species batch samples. Amplicon libraries were sequenced with Illumina Mi-seq and the sequencing reads were filtered and assembled using the software package mothur. Barcodes were then classified using a reference library comprised of Great Lakes fishes and potential invaders. In total, 34 species, including three at-risk species and three invasive species, were detected at the two sampling sites. This study shows the potential utility of metabarcoding for detection and identification of early life stage Great Lake fishes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".