Estimating the impacts of harvest distribution on road-building and snag abundance
Bibliographic record
Abstract
Various patterns of harvest in forests influence the length of road and number of stream crossings required. Snags are removed directly by harvesting, but they are also removed along road and opening edges to ensure worker safety. To assess the potential impacts of rate of harvest and pattern of harvest in an old-forest-dominated montane landscape, we developed a spatially explicit landscape dynamics model, which includes submodels for snag removal, harvesting activities, and access management. The model assesses the amount of new road construction and number of streams crossed by new roads, as well as changes in snag density and configuration across the landscape over a time horizon of several decades, in response to various harvesting patterns. We estimated that a dispersed 40-ha cutblock harvest pattern required about one-third more kilometres of new road over a 50-year period and removal of up to 70% more snags per hectare of harvest for safety purposes, compared with a harvest pattern based on natural-patch size distribution. Each 20% increase in stand-level retention resulted in a roughly equivalent increase in new road required. Up to eight times as many snags were removed per hectare of harvest for safety purposes at a stand-level retention of 70% than at a stand-level retention of 10%. The model appears to be an effective tool for determining the future impact of various harvest-pattern options on a number of important indicators of ecological impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".