Genetic tools for the conservation and management \nof red and grey squirrel populations
Bibliographic record
Abstract
The conservation of the native red squirrel (Sciurus vulgaris) in Britain and Ireland has been \ncomplicated by the presence of the invasive North American grey squirrel (Sciurus \ncarolinensis). In the recent years, the red squirrel has demonstrated a natural recovery in \nIreland, however, population reinforcement projects of the red squirrel are still required \nthroughout Britain and Ireland to maintain healthy populations due to a fragmented woodland \nlandscape. This project aims to demonstrate how conservation genetics can be used to support \nand inform practical management decisions for the long-term survival and management of red \nsquirrel populations. This is demonstrated through the assessment of populations nearly 20 \nyears post translocation in the west of Ireland and using a combination of survey techniques to \naccurately assess red squirrel abundance in Northern Ireland while applying multiple genetic \nmethods to assess contemporary and historical genetic diversity. Additionally, by measuring \ngenetic diversity levels this study assesses the overall impact of control efforts conducted \nbetween 2011 and 2020 on the grey squirrel population in north Wales. The study finds high \ngenetic diversity overall, with six diverse mtDNA haplotypes found and relatively high levels \nof nuclear genetic diversity. This suggests that ongoing grey squirrel control efforts may not \nadequately reduce genetic diversity to a level where it contributes to a long-term population \ndecline. In addition, two other case studies of the invasive grey squirrel are assessed in Scotland \nand Canada to show the requirement for tailored management plans. This project demonstrates \nthe need to gather all available information, including historical and contemporary, to support \npopulation reinforcement projects, and to effectively create tailored plans for control efforts of \nthe invasive species.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".