Data from: Metabolic scaling of individuals vs. populations: evidence for variation in scaling exponents at different hierarchical levels
Bibliographic record
Abstract
1. The power scaling of metabolic rate with body mass is fundamental to animal biology, due to the profound influence that animal size has on ecology and physiolatically with cell size or metabolic intensity between the boundaries of 0.67 and 1. Despite this tremendous interest in the value of scaling exponents, little is known about metabolic scaling within individual animals and how this relates to population-level scaling. 2. Here, we conducted a long-term study that repeatedly characterised the complete metabolic profile of a group of 68 individual fish (cunner, Tautogolabrus adspersus) by measuring their standard metabolic rate (SMR), routine metabolic rate (RMR), active metabolic rate (AMR), and aerobic scope (AS) in five separate trials over 10 months (fish mass range, 0.5–19.5 g). 3. At all levels of metabolic intensity, the mean exponents for the group of fish at any single point in time i.e., within trials; = 0.89, RMR = 0.89, = 0.94, AS = 0.96) were higher than those characterising the group as it aged i.e., across trials; = 0.82, = 0.84, = 0.90, = 0.92), and both were higher than the mean exponents for individual fish as they grew i.e., across trials but within individuals; = 0.74, = 0.79, = 0.83, = 0.85). 4. This variation in scaling relationships, which occurred at different hierarchical levels and across time as the fish aged, may have implications for bioenergetics and ecosystem modelling, and for individual ecological and physiological studies where body mass adjustments are made using specific scaling exponents to compare traits of interest between different-sized individuals. 5. The findings suggest that studies on fish population dynamics should apply metabolic scaling exponents that are significantly higher than those used in studies on individuals. However, the generality of this assertion should be confirmed by future work, as a few existing studies on endotherms (mainly birds) have reported shallower scaling relationships for basal metabolic rate between individuals as compared to within individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".