Translocating lions into an inbred lion population in the Hluhluwe‐iMfolozi Park, South Africa
Bibliographic record
Abstract
Abstract A fundamental problem in conservation biology is the risk of inbreeding in fragmented and declining populations. In the Hluhluwe‐iMfolozi Park (HiP), a small, enclosed reserve in South Africa, a large lion Panthera leo population arose from a founder group of five individuals in the 1960s. The HiP lion population went through a persistent decline and showed indications of inbreeding depression. To restore the genetic variation of the inbred HiP lion population, new lions were translocated into the existing population. Translocated females formed stable associations and established enduring pride areas with other translocated lionesses, but did not bond into native female prides. The translocated male coalition was more successful in gaining and maintaining residence in a pride than the translocated lone male that split off on his own from the male coalition. Litter size and cub survival was about twice as high for pairings involving at least one translocated parent than for pairings of two native lions. It is therefore possible to infuse new genes rapidly and successfully into a small, isolated lion population. Such translocations may become an important adaptive management tool as lion populations become increasingly fragmented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".