Lineage Identification and Genealogical Relationships Among Captive Galápagos Tortoises
Bibliographic record
Abstract
Genetic tools have become a critical complement to traditional approaches for meeting short- and long-term goals of ex situ conservation programs. The San Diego Zoo (SDZ) harbors a collection of wild-born and captive-born Galápagos giant tortoises (n = 22) of uncertain species designation and unknown genealogical relationships. Here, we used mitochondrial DNA haplotypic data and nuclear microsatellite genotypic data to identify the evolutionary lineage of wild-born and captive-born tortoises of unknown ancestry, to infer levels of relatedness among founders and captive-born tortoises, and assess putative pedigree relationships assigned by the SDZ studbook. Assignment tests revealed that 12 wild-born and five captive-born tortoises represent five different species from Isabela Island and one species from Santa Cruz Island, only five of which were consistent with current studbook designations. Three wild-born and one captive-born tortoise were of mixed ancestry. In addition, kinship analyses revealed two significant first-order relationship pairs between wild-born and captive-born tortoises, four second-order relationships (half-sibling) between wild-born and captive tortoises (full-sibs or parent-offspring), and one second-order relationship between two captive-born tortoises. Of particular note, we also reconstructed a first-order relationship between two wild-born individuals, violating the founder assumption. Overall, our results contribute to a worldwide effort in identifying genetically important Galápagos tortoises currently in captivity while revealing closely related founders, reconstructing genealogical relationships, and providing detailed management recommendations for the SDZ tortoises.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".