Life in the slow lane: Estimating the metabolic rate and trophic impact of the Greenland shark (Somniosus microcephalus)
Bibliographic record
Abstract
Metabolic rate is intricately linked to the ecology of organisms and can provide a framework to study the behaviour, life history, population dynamics, and trophic impact of a species. Acquiring measures of metabolic rate, however, has proven difficult for large water-breathing animals such as sharks, greatly limiting our understanding of the energetic lives of these highly threatened and ecologically important fish. The following thesis presents the first estimates of metabolic rate for one severly understudied and near-threatened species, the long-lived Greenland shark (Somniosus microcephalus). Resting and active routine metabolic rates were estimated through field respirometry conducted on four relatively large-bodied individuals (33-126 kg), including the largest individual shark studied via respirometry. Despite recording very low whole-animal resting metabolic rates, estimates were well explained by derived interspecies allometric and temperature scaling relationships. Combining these results with data acquired from biologger deployments on free-roaming sharks allowed for the estimation of field metabolic rates for individuals inhabiting the Eastern Canadian Arctic. The estimated low energy needs of Greenland sharks in the wild translated to equally low estimates of prey consumpion rate at the individual level. However, when assessed at the scale of localized populations in two coastal fjord ecosystems and across all of Baffin Bay, prey consumption by Greenland sharks is assumed to play a key role in the top-down regulation of Arctic marine food webs, though important data deficiencies must be addressed before final conclusions can be drawn.
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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".