Life in the slow lane: field metabolic rate and prey consumption rate of the Greenland shark (<i>Somniosus microcephalus</i>) modelled using archival biologgers
Bibliographic record
Abstract
Field metabolic rate (FMR) is a holistic measure of metabolism representing the routine energy utilization of a species living within a specific ecological context, thus providing insight into its ecology, fitness and resilience to environmental stressors. For animals that cannot be easily observed in the wild, FMR can also be used in concert with dietary data to quantitatively assess their role as consumers, improving understanding of the trophic linkages that structure food webs and allowing for informed management decisions. Here, we modelled the FMR of Greenland sharks (Somniosus microcephalus) equipped with biologger packages or pop-up archival satellite tags (PSATs) in two coastal inlets of Baffin Island (Nunavut) using metabolic scaling relationships for mass, temperature and activity. We estimated that Greenland sharks had an overall mean (±s.d.) FMR of 21.67±2.30 mg O2 h-1 kg-0.84 (n=30; 1-4 day accelerometer package deployments) while residing inside these cold-water fjord systems in the late summer, and 25.48±0.47 mg O2 h-1 kg-0.84 (n=6; PSATs) over an entire year. When considering prey consumption rate, an average shark in these systems (224 kg) requires a maintenance ration of 61-193 g of fish or marine mammal prey daily. As Greenland sharks are a lethargic polar species, these low FMR estimates, and corresponding prey consumption estimates, suggest they require very little energy to sustain themselves under natural conditions. These data provide the first characterization of the energetics and consumer role of this vulnerable and understudied species in the wild, which is essential given growing pressures from climate change and expanding commercial fisheries in the Arctic.
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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) | 0.001 | 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".