Bibliographic record
Abstract
Galicia is the most important forestry region of Spain, but its potential of producing the forest products is underdeveloped. A healthy and growing forestry sector could be an engine for regional and rural economic development, but forest management is impeded by forest ownership patterns. Most forests in Galicia are privately owned in small, scattered holdings that make it difficult to carry out the sustainable forest management required for forest sector development.A comprehensive sustainable forest management (SFM) strategy, based upon internationally recognized principles of sustainability, has been proposed as a means of rectifying the situation in Galicia. This strategy involves eight lines of actions that include such initiatives as improved legislation, increased public education and participation, and a new process for sustainable forest management that would be run by the government.This paper outlines the strategy, then goes on to describe new forest management processes and supporting technologies that are seen as necessary for promoting sustainable forest management in a region predominated by small forest ownerships. The new process will be based upon hierarchical and integrated forest management concepts, but will involve innovative approaches to regional and forest district management. The paper ends with a brief description of the initial steps that have been taken to implement the SFM Strategy of Galicia.
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 | 0.022 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".