Lineage identification of Galápagos tortoises in captivity worldwide
Bibliographic record
Abstract
Abstract Ex situ conservation strategies may be substantially informed by genetic data, and yet only recently have such approaches been used to facilitate captive population management of endangered species. The Galápagos tortoise Geochelone nigra is an endangered species that has benefited greatly from the application of molecular and population genetic data, but remains vulnerable throughout its range. The geographic and evolutionary origins of 98 tortoises in private collections and zoos on three continents were identified using mitochondrial DNA (mtDNA) control region sequences and multi‐locus microsatellite genotype data relative to a large database of representative samplings from all extant populations, including historical population allele frequency data for the Geochelone nigra abingdoni taxon on Pinta by way of museum specimens. All but six individuals had mtDNA haplotypes previously sampled, with the novel haplotypes identified as most closely related to robust populations on the islands of Santa Cruz and Isabela. Multi‐locus genotypic assignments corroborated the results obtained from the mtDNA analyses, with 83.7% of individuals consistently assigned to the same locality by both datasets. Overall, the majority of captive unknowns sampled were assigned to the La Caseta Geochelone nigra porteri population, with no fewer than six individuals of hybrid origin detected. Although a purported Pinta individual was revealed to be of Pinzón ancestry, the two females currently housed with Lonesome George exhibited haplotypic and genotypic signatures that indicate that they are among the most appropriate matches for captive breeding. More generally, molecular approaches continue to represent important tools for assessing conservation value, minimizing hybridization and guiding management programs for preserving the distinctiveness of G. nigra taxa in captivity.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".