Using spoor and prey counts to determine temporal and spatial variation in lion (<i>Panthera leo</i>) density
Bibliographic record
Abstract
In many African countries, large carnivores such as lions (Panthera leo (L., 1758)) are under serious threat through conflict with people, declining prey abundance, and exposure to disease. Spoor and prey count surveys were used to determine temporal and spatial variation in lion density in Khutse Game Reserve (KGR), Botswana, and the adjacent communal grazing area. Estimated lion density in KGR for the period September 2008 – June 2010 was 41% lower than for the period June 2007 – August 2008 (1.02 vs. 1.72 lions/100 km2). Prior to this population crash in mid-2008, estimated lion density in the communal grazing area (1.21 lions/100 km2) was 30% lower than inside KGR. The relative abundance of the three most abundant, preferred prey species of lions occurring in KGR decreased from 2001 to 2008 by 50%–79%. Based on two prey biomass estimates, the lion population in KGR was below the potential carrying capacity of the habitat after the crash in mid-2008. These results suggest that there could be a human-caused population sink around KGR, which might be strong enough to threaten the long-term survival of lions in the area; particularly if this edge effect is intensified by prey depletion and disease outbreaks, which might have caused the sudden decline in the lion population.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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)
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".