The Coastal Temperate Rainforests of Canada: The need for Ecosystem-Based Management
Bibliographic record
Abstract
The Central and North Coast and Haida Gwaii/Queen Charlotte Islands regions of British Columbia (B.C.) contain the world's largest remaining areas of intact coastal temperate rainforest. The region has been the focus of intense conflict among environmentalists, forestry companies, First Nations and other interests over the management of these high conservation value old growth forests. Recently completed land use planning processes have recommended increasing protection and improving forest practices on the rest of the landbase to more environmentally responsible methods defined by the guiding principles of Ecosystem Based Management (EBM). Based on an audit of logging plans (silvicultural prescriptions) approved between January 15, 2002 and February 24, 2003, The David Suzuki Foundation recently assessed current logging practices in the region. While forestry companies are not legally obliged to use the recommended EBM standards at this time, our assessment underscores how current logging practices fail to meet agreed-upon EBM standards. Firstly, clearcutting remains the dominant method of logging and where alternative methods have been attempted, in-block retention levels are low. Secondly, little effort has been demonstrated that would protect small fish-bearing streams (including salmonid bearing streams) or their tributaries within managed forest stands.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".