National News / Nouvelles nationales
Bibliographic record
Abstract
Canadian forest products companies are continuing their world-leading pace of achieving independent, third-party certification of their forest management activities.Up from 50 million hectares just four months ago, Canada now has a record 72 million hectares (or 179 million acres) of certified forests -the largest area of certified forest of any country in the world.This represents almost 43 percent of Canada's annual harvest of approximately 180 million m 3 and over 60 percent of Canada's managed forest lands."This strong performance among Canadian companies is clear evidence of broad industry commitment to sustainable forest management, meeting customer needs and assuring Canadians that our forests are well managed," said Jeffrey A. Hearn, President and CEO of Weldwood Canada Limited, who chairs FPAC's Environment and Forestry Steering Committee."All FPAC member companies with forest management responsibilities are seeking independent third-party audit certification."Having maintained over 90 percent of its original forest cover, Canada is already a global leader in balanced forest conservation, protection and use.Today, Canadian forest companies are embracing certification as a natural step in sustainable forest management. Why buyers are using certificationCertification is increasingly important to large wood and paper customers who want objective assurances that their supplies come from well-managed forests.Like financial audits, independent, thirdparty certification is a way of measuring and tracking sustainable forestry practices and holding a company accountable.Third-party independent audits ensure the stringent requirements of the standard are met.This in turn gives customers and communities the independent assurance that the forests are being well managed.Where international certification is going -Mutual recognition 11
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.095 | 0.019 |
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".