Policies and practices to sustain soil productivity: perspectives from the public and private sectors
Bibliographic record
Abstract
The USDA Forest Service, the Canadian Forest Service, and US and Canadian forest products industries are committed to the principles of sustainable forestry with a major focus on protecting soil productivity. The USDA Forest Service has developed and adopted soil quality standards to evaluate the effects of forest use and management activities on forest soils and, if necessary, prescribe remedial or preventive actions to avoid adverse impacts on soil productivity. Similarly, the Canadian Forest Service has adopted a series of criteria and indicators with which to monitor the impacts of management on soil resources. The policies of both public agencies reflect the recommendations of the Montréal Process Working Group (1999). Many forest industries have adopted the Sustainable Forestry Initiative developed by the American Forest and Paper Association (2000). Standards of the Sustainable Forestry Initiative clearly state the vision and direction for achieving sustainable forest management, goals, and objectives to be attained and performance measures for judging whether a goal or objective has been achieved. However, both public and private entities recognize that current standards, criteria, and indicators represent first approximations. Continuing revision and adjustment based on information from long-term research studies are vital to protecting soil productivity while deriving optimum public benefits from our forest-based resources.
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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".