Determining sex of Steller and California sea lions utilizing qPCR analysis of scat
Bibliographic record
Abstract
Developing methods to identify the sex of pinniped depositors from scat describes demographics of sampled populations and may reveal differential prey consumption between males and females. Sex-specific diet information determines predator impacts on prey populations and informs ecosystem management decisions. Matejusová (et al. 2013) developed qPCR methods to determine the sex of wild harbor seals (Phoca vitulina) from collected scat, which allowed scientists to document sex-specific diet differences across spatial and temporal scales in the Salish Sea (Schwarz et al. 2018). We expand on these methods by developing assays to determine sex from Steller (Eumetopias jubatus) and California (Zalophus californianus) sea lion scats. We acquired scat samples from California and Steller sea lion individuals of known sex housed in the Vancouver and Seattle aquariums. DNA was extracted using a QIAamp Fast DNA Stool Mini Kit and NucleoSpin® DNA Stool extraction kit. Novel Taqman gene expression assays were designed using distinct regions within the zinc-finger X-linked gene (ZFX) and sex-determining region Y gene (SRY) of Steller and California sea lions to be used in qPCR signal amplification. Once finalized, this protocol can be implemented to validate differential prey consumption of male and female Steller and California sea lions in wild populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".