Complex numerical responses to top-down and bottom-up processes in vertebrate populations
Bibliographic record
Abstract
Introduction The intrinsic rate of growth of animal populations ( r max ) is a speciesspecific character that is determined by a trade-off between reproductive capacity and survival. In simple form, given a finite amount of resources such as food and time, a species can evolve adaptations that either enhance reproduction and result in lower survival, or increase survival at the cost of lower per capita reproduction. These life-history features are related to body size in a wide range of animal species from protozoa to mammals, with r max negatively related to body size (Blueweiss et al . 1978; Caughley & Krebs 1983; Sinclair 1996). The species-specific adaptation, r max determines how species respond to environmental impacts. In a given environment, both large and small species experience the same negative environmental effects, and the degree to which the species are adapted to resist decline or tolerate them is reflected by r max . Body size buffers large mammals against environmental disturbance compared with smaller mammals, and this contributes to the greater apparent stability of large-mammal populations. Therefore, in mammals, population variability is inversely related to body size when considered over absolute time. However, when corrected for generation length, there is no relationship between population variability and body size. This implies that all species show the same intrinsic degree of population variability. Thus, when lifespan is taken into account, small species do not experience any more severe extrinsic perturbations than larger species (Sinclair 1996).
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".