Pilot study of a comprehensive resource estimation method from environmental DNA using universal D-loop amplification primers
Bibliographic record
Abstract
Many studies have investigated the ability of environmental DNA (eDNA) to identify the species. However, when individual species are to be identified, accurate estimation of their abundance using traditional eDNA analyses is still difficult. We previously developed a novel analytical method called HaCeD-Seq (haplotype count from eDNA by sequencing), which focuses on the mitochondrial D-loop sequence for eels and tuna. In this study, universal D-loop primers were designed to enable the comprehensive detection of multiple fish species by a single sequence. To sequence the full-length D-loop with high accuracy, we performed nanopore sequencing with unique molecular identifiers (UMI). In addition, to determine the D-loop reference sequence, whole genome sequencing was performed with thin coverage, and complete mitochondrial genomes were determined. We developed a UMI-based Nanopore D-loop sequencing analysis pipeline and released it as open-source software. We detected 5 out of 15 species (33%) and 10 haplotypes out of 35 individuals (29%) among the detected species. This study demonstrates the possibility of comprehensively obtaining information related to population size from eDNA. In the future, this method can be used to improve the accuracy of fish resource estimation, which is currently highly dependent on fishing catches.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".