Lion pride size versus feeding group size
Bibliographic record
Abstract
The attached dataset provides a sampled distribution of 3230 feeding groups of adult female lions in relation to size of the pride that those females belonged to in Serengeti National park, Tanzania. Ecological theory suggests that large social groups of carnivores should have reduced foraging efficiency because they encounter prey no more frequently than solitary hunters, but the entire group must share any prey they encounter. We developed behaviorally-based foraging models to show that fragmentation of large social groups into smaller hunting subgroups or mutual cooperation during hunting are both plausible hypothetical mechanisms capable of sustaining larger lion prides. The attached dataset from the Serengeti ecosystem demonstrates that lion prides typically fragment into small hunting groups that are well approximated by an exponential distribution of group sizes typical of fission-fusion social systems. A model linking fission-fusion group dynamics with predator-prey interaction predicts both the surprising degree of population stability of the Serengeti lions as well as the long-term persistence of large prides. There is little evidence, however, that Serengeti lions cooperate during hunting except when they hunt Cape buffalo, so fission-fusion is apparently the dominant stabilizing process in Serengeti.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".