The effects of ground-based harvesting on coastal British Columbia soils : mitigating the negative consequences
Bibliographic record
Abstract
The recent downturn in British Columbia’s coastal forest industry, together with an increased proportion of timber harvest from second growth stands has increased pressure on harvest operations to reduce costs. Using ground-based harvest methods, primarily with skidders and hoe-chuckers, harvest operations can lower costs compared to cable yarding. Skidders and hoe-chuckers have the potential to negatively affect future site productivity and natural hydrological processes. The complexity of factors influencing the amount of soil disturbance resulting from skidder and hoe-chucker use makes the interpretation of research results difficult. The strongest factors are soil moisture at time of harvest and soil texture. Short term research on seedling growth has found a decrease in growth on machine trails of 20 to 53 percent (Senyk & Craigdallie, 1997). The difference in growth between machine trails and undisturbed areas decreases over time. Increased growth on the margins of machine trails has been shown to partially offset losses to growth on trails. Direct hydrological impacts from ground-based machinery have not been researched in great depth as hydrological processes are also complex. Case studies have shown the potential of ground-based harvesting to change water flow patterns resulting in mass wasting and drainage structure failures. The negative of consequences of ground-based harvesting can be mitigated by reducing harvest during very wet periods and through the use of designated trails, rehabilitating trails, proper equipment choice and operator training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".