Operational and economic feasibility of logging within forested riparian zones
Bibliographic record
Abstract
The placement of riparian setbacks around water bodies has been shown to reduce logging impacts on aquatic and riparian communities and processes. However, the systematic application of no-harvest riparian setbacks can result in unnatural, linear patterns of older-growth forest across the landscape, a pattern that is inconsistent with the goal of emulating natural disturbances. Partial harvesting within riparian zones could provide a partial solution to this problem. As part of a larger project to evaluate the environmental consequences of partial harvesting within stream riparian zones of boreal mixedwood forests, we measured wood volumes removed from riparian zones and compared feller buncher productivity between partially harvested riparian zones and adjacent clearcut uplands. On average, from 20% to 33% of the total basal area (27% to 39% of the spruce/pine/fir basal area) was removed from the riparian zones. The riparian harvest resulted in considerable heterogeneity in residual stand structure, however, with basal areas within 50-m segments along the streams ranging from just over 50% to >95% remaining. Our results suggest that, even though the absolute effort required to harvest trees was greater in riparian zones, the larger average size of the trees more than compensated, so that the wood volume removed per unit effort was higher in riparian zones than in clearcuts. Key words: machine productivity, partial harvest, residual stand structure, riparian zone
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".