Bibliographic record
Abstract
Ireland is not only the least wooded country in Europe, it also has the lowest forest biodiversity. Despite this, forest research repeatedly demonstrates that Ireland is one of the most favourable locations for growing trees in Europe. Augustine Henry was acutely aware of these issues which provided a stimulus for his promotion of Irish forestry. Yet Irish forests have not always suffered such paucity. The further back we go in time, the greater the forest cover and diversity we discover. This paper traces the decline of Irish forests over many millennia and addresses the principal causes of this decline. Global climate change, human exploitation and geographical isolation are all contributory factors. The repeated glacial-interglacial cycle of climate change over the last two million years has decimated tree diversity in north-west Europe. The degree of Ireland 's isolation from Europe has varied as sea levels have changed in tandem with global ice volume. This has influenced tree migration rates and direction but the Irish Sea should be considered as a filter rather than a barrier to tree migration. Recent analysis of a large fossil pollen database is used to illustrate the migration of forest trees into Ireland following the most recent deglaciation. These migration models are compared to newly emerging genetic data on European and Irish oak diversity. Human exploitation of Irish forests over several millennia has radically reduced the amount of forest cover and has significantly impacted on what remains. Reconstructions of forest composition are used to place contemporary Irish forests in context. This context is relevant both to the maintenance of existing forests and the establishment of new ones.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".