Effects of manual brushing on 10-year survival and growth of Douglas-fir in the mixed broadleaf – shrub complex of southern interior British Columbia
Bibliographic record
Abstract
Manual brushing is used to minimize the competitive effects of paper birch (Betula papyrifera Marsh) and associated broadleaved trees on young Douglas-fir (Pseudotsuga menziesii var. glauca) in southern interior British Columbia. Effects of brushing broadleaved trees, predominantly birch, on interior Douglas-fir survival and growth were studied on four sites. Treatments were applied when plantations were five to nine years old. Through 10 years post treatment, brushing did not affect Douglas-fir survival, but increase height by 22 % and stem diameter by 31 % and the differences were greater than seen at five years. After 10 years, linear models described a declining Douglas-fir height or diameter with increasing broadleaved tree density. Boundary line analysis was used to describe maximum treatment response to broadleaved density and two distance independent competition indices for birch and broadleaves, combining either cover or density with relative heights (CRH, DRH, respectively). A negative exponential relationship was fit to 10-year Douglas-fir heights and diameters with increasing values CRH or DRH. Competition thresholds for density, CRH and DRH were not apparent. The quantile regression results indicated the 10-year response of young Douglas-fir diameter to brushing occurred primarily with the largest 55 % to 85 % of the population, CRH and DRH respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".