The impact of forest science in Chile: history, contribution, and challenges
Bibliographic record
Abstract
In this article we describe Chile’s transition from an agriculture productive model that originated in the 19th century into a more complex economic model that incorporates forest production, explaining the role of forest sciences in this process. Forest science has made great contributions to the country, especially in terms of improving forestation and forest management techniques that have allowed the rapid expansion of the forestry industry and prevented soil erosion on degraded lands. However, native forests have been neglected and vast areas of forest have been replaced with exotic plantations. This process has highlighted the imperative need for developing a new productive model to ensure not only a fair distribution of wealth but also the use of science-based sustainable forest management practices to protect native forest ecosystems nationwide. A national strategic plan for managing, conserving, and restoring native forests is needed not only to align the forest industry with sustainable development but also to develop sound climate change strategies to achieve the country’s goal of becoming carbon neutral by 2050. Under this scenario forest science can play an important role by producing much needed evidence-based knowledge.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".