Three forest landscape restoration projects to benefit Model Forests / Trois projets de restauration des paysages forestiers au profit des Forêts Modèles
Bibliographic record
Abstract
designed to develop models for secondary forest and degraded primary forest management within five Central American countries, with priority in Guatemala, Nicaragua and Costa Rica.The primary objective is to provide assistance to and help finance small and medium sizedforest enterprises in managing secondary and degraded forests through economically viable models.In the first phase, proponents will assess potential areas and enterprises to target for investments.After a feasibility study, the LuxDev will determine which enterprises to invest in.Many of the targeted countries also have Model Forests, several of which will be involved in project activities.As well, the Model Forest approach will be used, including facilitating the voluntary participation of representatives of stakeholder interests and values on the landscape to create a common vision and implementation strategy.The IMFN and the Ibero-American Model Forest Network is pleased to be a partner in Initiative 20 x 20 and contribute to FLR in the region.This story has been adapted from the Ibero-American Model Forest Network's Web site.For more information: Heather.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".