Planning for success: Serengeti lions seek prey accessibility rather than abundance
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Insufficient payload (model declined to judge)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.003
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.268 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Summary We used long‐term radio‐telemetry data to investigate how Serengeti lions ( Panthera leo ) distribute themselves with respect to hunting opportunities. Specifically, we investigate whether lions hunt in areas where prey are easy to capture or where prey are locally abundant. We used resource‐selection functions (logistic regressions) to measure the location of kills/carcasses with respect to five different habitats: the view‐sheds from large rocky outcrops, river confluences, woodland vegetation, erosion embankments and water sources. As expected for a sit‐and‐wait predator, resting lions spent more time in areas with good cover. On a broad‐scale, lions shifted their ranges according to the seasonal movement of prey, but at a finer scale (< 100 m) lions fed in areas with high prey ‘catchability’ rather than high prey density. Plains lions selected erosion embankments, view‐sheds from rocky outcrops, and access to free water. Woodland lions tended to use erosion embankments, and woody vegetation. The results emphasize the importance of fine‐scale landscape and habitat features when assessing predator–prey theory and conservation.
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.
The record
- Venue
- Journal of Animal Ecology
- Topic
- Wildlife Ecology and Conservation
- Field
- Environmental Science
- Canadian institutions
- University of British Columbia
- Funders
- Natural Sciences and Engineering Research Council of CanadaNational Science Foundation
- Keywords
- PredationWoodlandHabitatPantheraGeographyEcologyVegetation (pathology)Abundance (ecology)SnagPredatorBiology
- Has abstract in OpenAlex
- yes