Food Web Structure in the Canadian Subarctic: Implications for Harp Seals
Bibliographic record
Abstract
The Arctic is warming by nearly four times the rate of the global average, leading to changing ocean conditions that have far-reaching consequences for marine ecosystems. Effective ecosystem-based management strategies are therefore essential for preserving the health and resilience of these ecosystems amidst the challenges posed by climate change. Subarctic regions serve as an interface between Arctic and temperate waters, offering a unique and accessible setting to observe marine populations at the fringes of their respective ecosystems and to monitor their responses to environmental shifts. The Newfoundland shelf in the Northwest Atlantic (NWA) is a highly productive region of the subarctic, supporting multiple commercial fisheries, that has experienced considerable restructuring of its marine food web since the early 1990s. Recent observations of variability in the ice conditions (e.g., timing of retreat, extent and thickness), zooplankton community composition, fish biomass and marine mammal population dynamics highlight the need for a holistic analysis of the NWA shelf food web that incorporates multiple functional groups and trophic levels. For this PhD thesis, I used stable isotope analysis of bulk tissues and individual amino acids to: (1) Quantify trophic discrimination factors (TDFs) for multiple functional groups and tissues, and found that TDFs did not differ between tissues, but did decrease with increasing trophic position (TP), being largest in zooplankton (6.9‰) and lowest in harp seals (3.1‰). Though speculative, the mechanisms likely responsible for this are diet quality and mode of excretion (urea/uric acid vs. ammonia). This has implications for accurate calculations of TP and our interpretations of food web structure more broadly. (2) Characterise the structure of the NWA food web between 2012 and 2018. The isotopic baseline, as proxied by the
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".