Managing ash in farm woodlots: some suggested prescriptions
Bibliographic record
Abstract
Ash (Fraxinus sp.) is an important component of upland sites and is dominant in lowland sites in Southwestern Ontario. While information on emerald ash borer (Agrilus planipennis) (EAB) and its signs and symptoms is readily available, there is little on management options that consider EAB affects. This paper was developed from a woodlot tour designed to transfer knowledge to farmers of good forestry and stewardship practices for managing ash. Three generic strategies for certain stand types and four site-specific prescriptions for woodlots in anticipation of EAB infestation are presented. The generic strategies can be considered when developing a prescription for ash-dominant lowlands. They apply to stands infested with EAB and where EAB is expected in 5 to 10 years, or 10 years or more. The site-specific prescriptions are examples that describe applicable issues, strategies, and objectives in more detail. The proportion and size distribution of ash and the number of years anticipated before infestation are important considerations in optimizing ash growth and value and mitigating the impact of EAB on forest structure, value and function. If EAB infestation is expected in 10 years or more, three or four stand entries may be possible to influence the future forest.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".