Estimation of tuna population by the improved analytical pipeline of unique molecular identifier-assisted HaCeD-Seq (haplotype count from eDNA)
Bibliographic record
Abstract
Many studies have investigated the ability to identify species from environmental DNA (eDNA). However, even when individual species are identified, the accurate estimation of their abundances by traditional eDNA analyses has been still difficult. We previously developed a novel analytical method called HaCeD-Seq (Haplotype Count from eDNA), which focuses on the mitochondrial D-loop sequence. The D-loop is a rapidly evolving sequence and has been used to estimate the abundance of eel species in breeding water. In the current study, we have further improved this method by applying unique molecular identifier (UMI) tags, which eliminate the PCR and sequencing errors and extend the detection range by an order of magnitude. Based on this improved HaCeD-Seq pipeline, we computed the abundance of Pacific bluefin tuna (Thunnus orientalis) in aquarium tanks at the Tokyo Sea Life Park (Kasai, Tokyo, Japan). This tuna species is commercially important but is at high risk of resource depletion. With the developed UMI tag method, 90 out of 96 haplotypes (94%) were successfully detected from Pacific bluefin tuna eDNA. By contrast, only 29 out of 96 haplotypes (30%) were detected when UMI tags were not used. Our findings indicate the potential for conducting non-invasive fish stock surveys by sampling eDNA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".