Meeting visual quality objectives with operational radial-strip partial cutting in coastal British Columbia: A post-harvest assessment
Bibliographic record
Abstract
The spectacular aesthetic quality of British Columbia's coastal forests has long been an issue for forest managers who must often meet government-imposed visual quality objectives. The result of such objectives within traditional clearcutting silvicultural systems has often been large reductions in otherwise allowable timber removals. In this document we present the results of a post-harvest analysis of an operational trial using radial-strip partial harvesting in a highly visually sensitive area of coastal British Columbia. The analysis confirmed that this innovative form of partial harvesting successfully met visual quality requirements, while concurrently permitting significantly more timber extraction, in an operationally feasible manner, than would have been available under a traditional clearcutting system. We attribute the visual success of this harvest method in a highly visually sensitive area primarily to: (1) the radial pattern of linear clearings where it is not possible to see more than a few cleared strips from any one vantage point; (2) the avoidance of a regular series of parallel geometric clearings; (3) the narrow clearing width, which maximises bare ground screening; (4) feathered edges, which avoided highly contrasting edges of the strips; and (5) green tree retention across age classes. Faller and management experience with this method is expected to result in higher productivity and lower costs in the future. This harvesting technique appears to present a viable alternative to clearcutting for forest managers working in visually sensitive areas. Future work should focus on understanding and determining impacts on other factors such as ecological values and silvicultural goals. Key words: aesthetics, partial-cutting, simulation, VIA, visual impact assessment, VQO, visual quality objective
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".