A major shift to the retention approach for forestry can help resolve some global forest sustainability issues
Bibliographic record
Abstract
Abstract Approximately 85% of the global forest estate is neither formally protected nor in areas dedicated to intensive wood production (e.g., plantations). Given the spatial extent of unprotected forests, finding management approaches that will sustain their multiple environmental, economic, and cultural values and prevent their conversion to other uses is imperative. The major global challenge of native forest management is further demonstrated by ongoing steep declines in forest biodiversity and carbon stocks. Here, we suggest that an essential part of such management—supplementing the protection of large reserves and sensitive areas within forest landscapes (e.g., aquatic features)—is the adoption of the retention approach in forests where logging occurs. This ecological approach to harvesting provides for permanent retention of important selected structures (e.g., trees and decayed logs) to provide for continuity of ecosystem structure, function, and species composition in the postharvest forest. The retention approach supports the integration of environmental, economic, and cultural values and is broadly applicable to tropical, temperate, and boreal forests, adaptable to different management objectives, and appropriate in different societal settings. The widespread adoption of the retention approach would be one of the most significant changes in management practice since the onset of modern high‐yield forestry.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".