Sustained timber yield claims, considerations, and tradeoffs for selectively logged forests
Bibliographic record
Abstract
What is meant by sustainability depends on what is sustained and at what level. Sustainable forest management, for example, requires maintenance of a variety of values not the least of which is sustained timber yields (STYs). For the 1 Bha of the world's forests subjected to selective or partial logging, failure to maintain yields can be hidden by regulatory requirements and questionable auditing practices such as increasing the number of commercial species with each harvest, reducing the minimum size at which trees can be harvested and accepting logs of lower quality. For assertions of STY to be credible, clarity is needed about all these issues, as well as about the associated ecological and economic tradeoffs. Lack of clarity about sustainability heightens risks of unsubstantiated claims and unseen losses. STY is possible but often requires cutting cycles that are longer and logging intensities that are lower than prescribed by law, as well as effective use of low-impact logging practices and application of silvicultural treatments to promote timber stock recovery. These departures from business-as-usual practices will lower profit margins but generally benefit biodiversity and ecosystem services.
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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".