Current trends in forestry research of Latin-America: an editorial overview of the Special Issue
Bibliographic record
Abstract
Abstract Mendoza city (Argentina) hosted the VIII Latin American Forestry Congress (CONFLAT) and the V Forestry Congress of Argentina (CFA) in 2023, where relevant issues were addressed, such as climate change, degradation, reforestation, management and forest industry, monitoring, environmental services, social issues, and governance, among others. The objective of this Special Issue was to present the main advances in Forestry Science for Latin-America in the context of changing governance and forest livelihoods for people. The fifteen articles emphasize the interdisciplinary nature of the forest management and conservation, and that multiple variables must be considered to achieve sustainability. The articles come from studies across Southern South-America (Argentina, Brazil, Chile, and Uruguay), and the collaboration of researchers of other countries (México, Canada, and Spain). Articles include research in tropical, Mediterranean and temperate Sub-Antarctic forests. Together, these articles provide a snapshot of new forestry research carried out locally and internationally to bring about beneficial ecological and environmental outcomes in a world facing the challenges of sustainable management and conservation amongst the threats and uncertainty of climate change and environmental degradation responsible for extensive loss of biodiversity and environmental services. We believe that this Special Issue will encourage more inter-disciplinary research focusing on management and conservation of forests.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".