Sexing From Seawater: Application of Environmental <scp>DNA</scp> Beyond Species Detection for Cetaceans
Bibliographic record
Abstract
ABSTRACT Anthropogenic pressures on marine ecosystems are intensifying, highlighting the need to bridge knowledge gaps for species at risk. Data deficiencies, particularly for species recovering from historic declines, such as marine megafauna like whales, hinder effective management. Understanding long‐term population viability and identifying any sex‐biased threats is important for cetacean conservation. Killer whales ( Orcinus orca ) and humpback whales ( Megaptera novaeangliae ) are well‐studied in the Northeast Pacific, making them ideal for optimizing noninvasive environmental DNA (eDNA) techniques for sex determination. We collected eDNA flukeprint samples from killer whales ( n = 67) and humpback whales ( n = 18), analyzing ZFX/ZFY gene amplification using conventional PCR to compare results against known sexes. Samples from killer whales exhibited higher ZFX/ZFY PCR amplification success (53%) compared with humpback whales (44%). However, the close social structure of this species likely contributed to only 54% of samples matching the known sex of whales sampled. Conversely, humpback whale samples accurately matched the known sexes of individuals (100%). These findings demonstrate eDNA's potential to replace more invasive biopsies for sex determination but highlight the need for further optimization regarding sampling protocols and species‐specific ZFX/ZFY amplification approaches. Additionally, eDNA flukeprint sampling also shows promise for other solitary cetaceans such as large rorquals ( Balaenoptera spp.), which remain among the most data‐deficient species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".