Soil disturbance and juvenile Douglas-fir growth following stump removal on moderately coarse textured soils in southwestern British Columbia: 10-year results
Bibliographic record
Abstract
Mechanized treatment for root diseases such as Coniferiporia sulphurascens (Pilát) L. W. Zhou & Y. C. Dai (syn. Phellinus sulphurascens Pilát) and Armillaria ostoyae (Romagn.) Herink (syn. Armillaria solidipes Peck) is often avoided due to cost or the perception that removal of stumps creates detrimental soil disturbance or degradation that hinders site productivity. Our study tested five diseased stands that were treated by extracting stumps following harvesting and replanted with susceptible Douglas-fir. Soil disturbance surveys were conducted in treated and untreated plots, and individual planted spots were assessed and categorized for soil disturbance using existing and proposed new categories specific to disturbance caused by the stump removal. Tree measurements were taken at intervals over the first 10 years of stand development, and foliage was sampled for nutrient analysis. The percentage of total and counted disturbance was 20%–46% and 8%–11% greater, respectively, in treated versus untreated plots; however, mean tree growth in height and diameter was not statistically different between treatments and was more positive for treated plots at all sites but one. Tree nutrition and survival to age 10 was not negatively affected by stump removal. Total site productivity represented by basal area and tree volume differed widely between sites but was not significantly different between treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".