Variability and size in mammals and birds
Bibliographic record
Abstract
Body size, its variability, and their ecological correlates have long been important topics in evolutionary biology. Yet, the question of whether there is a general relationship between size and size-relative variability has not previously been addressed. Through an analysis of body-mass and length measurements from 65 074 individuals from 351 mammalian species, we show that size-relative variability increases significantly with mean species body size. Analysis of mean body mass and standard deviations for 237 species of birds revealed the same pattern. We present three plausible alternatives explanations and eliminate several others. Of these, the hypothesis that the increase in size-relative variability with mean body mass is related to the scaling of body mass components is most strongly supported. In effect, larger mammals and birds are more variable because their body mass is composed to greater relative degree of components with higher intrinsic variability (bone, fat, and muscle). In contrast, smaller mammals and birds have lower body mass variability because they are composed to a greater relative extent of components (viscera and nervous system) in which size variation is more highly constrained by energetic and functional factors.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".