Perspectives of forest practitioners on climate change adaptation in the Yukon and Northwest Territories of Canada
Bibliographic record
Abstract
Forestry practitioners in the Yukon and Northwest Territories of Canada were asked to complete a questionnaire examining the likely impacts of climate change on forest sector sustainability and adaptation options to climate change. Practitioners were asked to self-assess their knowledge on various aspects of climate change and ranked their level of knowledge as generally only poor to fair, despite past educational efforts in this area. Changes in the intensity, severity or magnitude of forest insect outbreaks, changes in extreme weather events, and changes in the intensity, severity or magnitude of forest fires were the three impacts most frequently identified as having had an impact on sustainability. More than half of the respondents indicated that commodity prices, availability of timber, trade policies, environmental regulations, and the ability to secure needed capital as presently having more of a negative impact on sustainability than climate change. The assessment of 65 potential adaptation options was structured according to the criteria of the Montreal Process. The majority of respondents considered the goals of adaptation to be synonymous with the criteria of sustainable forest management, indicating the Montreal Process criteria provide a suitable framework for assessing adaptation options in the forest sector. The intensity, severity and magnitude of forest insect outbreaks under future climate conditions, forest growth and productivity, precipitation, climate variability and the intensity, severity and magnitude of forest fires were ranked as the most important areas where further information would be of assistance to decision-making. Key words: climate change, adaptation, boreal forest, forestry, Yukon, Northwest Territories
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".