Cutting versus herbicides: Tenth-year volume and release cost-effectiveness of sub-boreal conifer plantations
Bibliographic record
Abstract
Few cost-effectiveness studies of vegetation management in conifer plantations are reported in the literature. This study provides follow-up cost-effectiveness analysis from research conducted at the Fallingsnow Ecosystem Project in northwestern Ontario, Canada with the objective of determining the relationship between release treatment costs and planted white spruce (Picea glauca [Moench] Voss) stem volume ($ m -3 ) ten years after alternative release treatments. Treatment cost estimates for 2003 were calculated by applying 1993 time-study data to estimated 2003 market costs for each treatment component. Untreated control plots had no treatment costs and were not included in the analysis. Including them will always suggest that doing nothing will be the most cost-effective, regardless how limited spruce volume is. The most cost-effective treatment was the aerial application of herbicide Vision ($12.16 m -3 ), followed by the aerial application of herbicide Release ($12.18 m -3 ), cutting with brushsaw ($38.38 m -3 ) and mechanical tending by Silvana Selective ($42.65 m -3 ). No cost differences were found between the herbicide treatments (p = 0.998) or between the cutting treatments (p = 0.559). The herbicide treatments were three-fold more cost-effective than the cutting treatments (p = 0.001). This analysis only considered the planted conifer component of these young stands. Key words: clearing saws, competition, forest vegetation management, glyphosate, Great Lakes – St. Lawrence Forest, herbicide alternatives, mixedwood, pesticide, release treatment, triclopyr, weed
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".