Explorative statistical analysis to identify factors affecting the survival of pygmy hippopotamus (Choeropsis liberiensis) kept in zoological gardens
Bibliographic record
Abstract
This study aimed to evaluate the genetic and environmental factors affecting the survival of the pygmy hippopotamus (Choeropsis liberiensis) population kept in zoological gardens, including the estimation of the inbreeding level. Survival was examined at three time points: birth (0–1 days), 1 month (30 days) and 5 years of age. The data were obtained from the Zoological Information Management System, and covered observations on 1196 pygmy hippo, collected between 1873 and April 2016 and grouped into five geographical regions (Africa, Australasia, Europe, USA and Canada, and South America). The analyses were performed using LOGIT and PROBIT transformations of the binominal survival data. No statistically significant differences (P-value > 0.05) were observed between the results obtained from the two transformations. The survival of individuals was found to decrease with increased level of inbreeding, the most significant effect being on survival at birth. At all three survival time points the highest survival was observed among females. Additionally, the Australasia region showed the highest overall survival. We hypothesize that the environment, climate and similarity of local food to the native diet of pygmy hippo allows animals kept in zoological gardens in that region to perform better than in other regions. The results of this study indicated that it is necessary to exercise vigilance in reducing inbreeding as it negatively impacts survival. This can be achieved by continued efforts to maintain and improve genetic diversity through management of breeding and associated transfer of animals between zoological facilities to reduce relatedness in breeding pairs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".