Temporal Monitoring of the Floreana Island Galapagos Giant Tortoise Captive Breeding Program
Bibliographic record
Abstract
Captive breeding programs benefit from genetic analyses that identify relatedness between individuals, assign parentage to offspring, and track levels of genetic diversity. Monitoring these parameters across breeding cycles is critical to the success of a captive breeding program as it allows conservation managers to iteratively evaluate and adjust program structure. However, in practice, genetic tracking of breeding outcomes is rarely conducted. Here, we examined the first three offspring cohorts (2017-2020) of the genetically informed captive breeding program for the Floreana Island Galapagos giant tortoise, Chelonoidis niger. This captive breeding program is unique as the Floreana tortoise has been extinct since the 1800s, but its genome persists, in part, in the form of living hybrids with the extant Volcano Wolf tortoise, C. becki. Breeding over the study period took place at the Galapagos National Park Directorate breeding facility in four corrals, each containing three females and two males. Using 17 microsatellite markers, we were able to assign parentage to 94 of the 98 offspring produced over the study period. We observe that despite the addition of more founders since the pilot breeding program, the effective population size remains low, and changes to the arrangements of breeding corrals may be necessary to encourage more equal reproductive output from the males. This study demonstrates the value of hybrids for species restoration and the importance of continually reassessing the outcomes of captive breeding.
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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 | 0.001 | 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".