Integrating Protected Areas, Plantations, and Certification
Bibliographic record
Abstract
SUMMARY. The World Wildlife Fund (WWF) has conducted significant work in the areas of forest conservation and sustainable management. The main findings of WWF&s;s Howard and Stead (2001) , as outlined in The Forest Industry in the 21st Century report, make the case for meeting the world&s;s forest products needs from one-fifth of the world&s;s forest estate. In addition, WWF&s;s experience in the realm of forest management certification sheds light on certification&s;s potential to ensure conservation benefits from plantations and help overcome some of the present challenges faced by plantation forestry. Recognizing protected area needs (particularly in the US and Canada) on an ecoregional level and helping to define and identify High Conservation Value Forests (HCVFs) are also important components of WWF&s;s work in forest conservation. Within the context of the plantations and protected areas debate, some of WWF&s;s research and analysis suggests that the pace and scope in which the projected expansion of fast-growing tree-plantations can be developed within the court of public opinion will be determined largely by commensurate efforts to se 16 cure adequate protected areas and the safeguarding of forest found to be of high conservation value. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: < docdelivery@haworthpress.com > Website: < https://www.HaworthPress.com > © 2005 by The Haworth Press, Inc. All rights reserved.]
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".