Soil disturbance and aspen regeneration on clay soils: Three case histories
Bibliographic record
Abstract
Sustaining forest productivity requires maintaining soil productivity and prompt establishment of adequate regeneration following harvest. We determined effects of commercial, winter-logging of aspen-dominated stands on soil disturbance and development of regeneration on three sites with clay soils. We established transects across each site, recorded pre-harvest stand information, post-harvest site disturbance, and first-year aspen sucker density and height. Use of large logging equipment produced heavy disturbance on 38% of a well-drained site; 45% of the area had no aspen suckers and 82% had less than the recommended minimum of 15 000 (15 k) suckers per ha (6 k ac−1). Mean height of dominant suckers was 45 cm (18 in). Hand felling and a small skidder caused heavy disturbance on 12% of a moderately well-drained site. Sucker density averaged 34 k ha−1 (14 k ac−1) and height was 97 cm (38 in). Cut-to-length (CTL) equipment produced heavy disturbance on 11% of a somewhat poorly-drained site, mean sucker density of 24 k ha−1 (9.6 k ac−1), and height of 101 cm (40 in). These severely disturbed areas essentially are removed from the aspen-producing land base. Retaining the northern hardwood and conifer growing stock would result in less site disturbance and help maintain natural hydrologic and nutrient cycling processes. Key words: aspen management, site disturbance, sustainable management, logging damage, soil rutting, root damage, evapotranspiration, soil aeration, clearcutting with residuals
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".