Changes in the impact and control of an invasive alien: the grey squirrel (<i>Sciurus carolinensis</i>) in Great Britain, as determined from regional surveys
Bibliographic record
Abstract
The grey squirrel, Sciurus carolinensis Gmelin, was introduced into sites in England, Wales, Scotland and Ireland from the United States and Canada between 1876 and 1929. Soon after its introduction there were reports of damage to trees by seasonal bark stripping activity. Surveys in state and private forests since 1954 have monitored their distribution and impacts. Two surveys also gathered information on control efforts used to minimise damage. Grey squirrel population range has expanded significantly in Britain over the last 50 years and continues to do so. Survey results show high variability between years in damage recorded, consistent with the understanding that damage is triggered by high numbers of juveniles entering the population following a good breeding season. Results also show high variability between tree species in levels of damage recorded, but that thin-barked tree species are most at risk of damage from grey squirrels. Further, results show that the economic cost of damage can be high and that control measures will be ineffective if not appropriately targeted. The findings support suggestions that grey squirrels in mainland Europe should be eradicated to prevent future population expansion and any accompanying impacts on commercial timber crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".