Bibliographic record
Abstract
The economic contributions from commercial forestry, measured in trade terms, employment and regional development, are well established. Less understood is the environmental contribution of forestry, provided that forestry is practised in a sustainable manner. Despite the economic and environmental benefits, the social license for commercial forestry is increasingly challenged in terms of access to timber and the conditions placed on access, and in access to major export markets for forest products. Fundamental to addressing these challenges is the utilization of harvesting regimes acceptable to both resource owners and consumers. Clearcut harvesting may be a scientifically reasonable replication of natural disturbance, allowing adequate provision for forest character and structure, but it is the emotional impact of the harvest site that often determines public acceptability.The institutional setting for commercial forestry is evolving rapidly and is increasingly driven by non-governmental groups that are proving particularly adept with information age tools. This paper will examine the supply and demand factors that are producing the pressure on harvesting practices, the institutional response to these pressures, the physical and financial implications of partial-cut harvesting, and will examine the harvesting norms that have emerged in a number of key softwood producing regions. Key words: clearcutting, partial-cut harvesting, forest management, forest policy, marketing
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".