Profitability of manual brushing in young lodgepole pine plantations
Bibliographic record
Abstract
Manual brushing is an important silvicultural tool commonly used to control competing vegetation in young conifer plantations. Yet little is known about the short-term economic benefits of one versus two brushing treatments. Using forest establishment data from the Fraser Lake and Bednesti areas of the Central Interior of British Columbia, we examined the profitability of one and two applications of brushing treatments under different internal rates of return (IRR) and three brushing radii (0.75, 1.00, and 1.25 m). Our results showed that one year of brushing treatment would be profitable for almost all brushing radii since profitability required only a short reduction in cutting age and lower IRRs. Applying two consecutive years of brushing would clearly require either higher discount rates or a longer waiting period for the brushing to be profitable. We believe that the approach described in this paper will assist forest practitioners when analyzing the value of brushing in terms of return on investment over time. The economic framework will also assist forest practitioners when deciding on brush control options for young conifer plantations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".