The scaled sardine’s unique metabolic phenotype and its implications for the susceptibility of small tropical pelagic fishes to climate change
Bibliographic record
Abstract
Small pelagic fishes (e.g., sardines, anchovies and their relatives) are preyed upon by large predatory fishes, birds and mammals, and thus, are key species in marine food webs and with respect to ecosystem health and productivity. However, we know little about their physiology, and such information will be critical to predicting how their populations may be impacted by human-induced rapid environmental change (HIREC) and in implementing effective conservation strategies. As a first step, we determined the maximum swimming speed, aerobic capacity [maximum metabolic rate (MMR) and aerobic scope (AS)] and cost of transport (COT; the energy required to swim a given distance) of scaled sardines (Harengula jaguana) collected in Eleuthera (The Bahamas). The scaled sardine’s critical swimming speed (Ucrit) was ~ 5–6 body length’s per second, and this agrees with data collected on free-swimming schools of similar fishes in the wild. However, they had unexpectedly high values for MMR and AS (~ 25% and 70% greater than tuna, respectively), and for COT. These findings have important implications with regard to how these ecologically important fishes will potentially respond to HIREC-related challenges such as increased temperature and decreases in the biomass and size of plankton upon which they feed.
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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".