Implementing Forest Certification on Newfoundland and Labrador Crown Lands: An Evaluation of Government and Industry Perspectives
Bibliographic record
Abstract
As of December 2012, approximately 148 million hectares of forestland in Canada have \nbeen certified to a third-party forest certification standard. In Newfoundland and \nLabrador, the only Crown forests that have been certified are under the management of \nthe province’s only pulp and paper mill. In order to evaluate the possibility and \npracticality of implementing certification on all provincial Crown lands, this study \nsurveyed forestry stakeholders from the provincial forest service, pulp and paper industry \nand sawmill/product industry to uncover their views on this topic and determine whether \nthey share complementary forest certification goals. Overall, the majority of respondents \nagreed that certification should be pursued and favoured a joint government-industry \napproach to leading and financing this initiative. In keeping with previous studies, no \nmajor barriers to implementing certification were uncovered, and therefore it is \nrecommended that government and industry work closely together to develop and \nimplement a provincial certification plan.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".