Bibliographic record
Abstract
ABSTRACT Canada's forests cover more than 418 million hectares, 45% of our land area, and represent a diverse range of ecosystems including mixed hardwood stands in southeastern Canada, temperate rain forests of British Columbia and dwarf forests of the arctic tundra. The size and diversity of the forest presents interesting challenges to forest health assessment. The Canadian Forest Service (CFS) now recognizes that “forest health” encompasses more than the incidence and distribution of insect pests and diseasecausing organisms and that forests are perceived as healthy when ecological processes are maintained and pest populations are operating within natural ranges of variability. Different stakeholders are also seen as having varying definitions of forest “health.” For example, the forest industry may focus on health as it relates to timber productivity, whereas environmental advocates may focus on ecological integrity. The assessment and reporting of forest health in Canada is currently being accomplished through cooperation among federal and provincial governments, academia, and industry. An interagency program is being developed where broad‐scale geographic coverage will be linked to national forest inventory plots, current pest conditions monitored by provincial agencies, research on specific disturbance agents carried out regionally, and an overall synthesis of national forest health undertaken by the Canadian Forest Service.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".