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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".