Diet characterization of Hudson Bay ringed seals in a changing sub-arctic marine environment
Bibliographic record
Abstract
Rapid environmental change in the Arctic can influence food web structure and dynamics. Ice- obligate species, such as ringed seals (Pusa hispida), are particularly vulnerable to decreased sea ice extent. In this thesis, I examined the patterns and foraging plasticity of ringed seals in Hudson Bay, an ecosystem undergoing rapid ecological change, to make inferences on possible changes to the Arctic marine food web dynamics. To examine ringed seal diet, I used muscle and liver tissues from subsistence harvested seals, and vibrissae segments from live captured ringed seals. Specifically, I investigated spatial, temporal, and within-individual variation in the diet of ringed seal populations. I used long-term datasets from two Nunavut communities over the years of 2003 – 2017 to quantify the general increase in isotopic niche breadth for seals in western Hudson Bay and a decrease in eastern Hudson Bay, despite having similar diet proportions. Using chronological tissue sampling, I then revealed high variation among individuals rather than within individuals of seals in the same age and sex class, indicating that these generalist feeders are foraging with individualized strategies. Lastly, I compared ringed seal diet signatures with two other Hudson Bay phocid species and indicated that ringed seal diets do not generally overlap with harbour seals (Phoca vitulina). I then determined reliable estimates of sympagic and pelagic algal carbon sources to the diets of the three seal species and found that ringed seals have more pelagic algal content and less sympagic algal carbon in their diet compared to harbour seals and bearded seals (Erignathus barbatus). Overall, this thesis highlights the limits to foraging plasticity and spatio- temporal feeding patterns of ringed seals in a changing environment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".