Bibliographic record
Abstract
List of contributors Preface to Volume 1 Preface to Volume 2 Acknowledgements 1. History and Impact of Forest ManagementPart 1: Sustaining Forest Influences2. Forest Reserves, Parks and Wilderness: An Australian Perspective3. Forests as Protection from Natural Hazards4. Interventions to enhance the conservation of biodiversityPart 2: Sustainable Wood Production5. assessing Potential Sustainable Wood Yield6. Silvicultural Treatments to Enhance Productivity7. Sustainable Management of Soil and Site8. Management of Pest Threats9. Management of the Disease BurdenPart 3. Sustaining Social Values and Benefits10. Working with Forest Stakeholders11. The New Forest Policy and Joint Forest Management in India12. Trees in the Urban EnvironmentPart 4: Case Studies of Sustainable Management13. The Structure, Functioning and Management of Old Growth Cedar/Hemlock/Fir Forests on Vancouver Island, British Columbia14. The Beech Forests of Haute-Normandie, France15. Restructuring of Plantation Forest, Kielder, UK16. Sustainable Management of the Mountain Ash Forests in the Central Highlands, Victoria, Australia17. Sustainable Management of Malaysian Rain Forest18. Sustainable Plantation Forestry: A Case Study of Wood Production and Environmental Management Strategies in the Usutu Forest, Swaziland Synthesis and Conclusions Index
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.091 | 0.037 |
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".