Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 6 – Estimating the economic benefits
Bibliographic record
Abstract
The Green River precommercial thinning (PCT) trials were established between 1959 and 1961 in naturally regenerating balsam fir (Abies balsamea [L.] Mill.)-dominated stands an average of eight years after overstory removal. Following clearcut harvest of three of the study's six replicates in the fall of 2008, the rotation-length effects of PCT and vegetation management (VM; deciduous tree and brush suppression) on the forest value chain were integrated into a spreadsheet-based model that estimates the net present value (NPV) of these silvicultural treatments. Assuming costs and prices near recent values, both PCT and VM were observed to support positive landowner NPVs through discount rates in excess of 6%. At a discount rate of 4% and an age where sawlog production was maximized, PCT and VM offered similar NPV (>$550/ha). Landowners that can attract buyers willing to pay a premium for wood from thinned stands (equal to the sum of reduced operational overhead charges, harvesting and sawmilling costs and increased pulping costs) could see the value of their stands double. Sensitivity analyses revealed that these results are dependent on site productivity, silvicultural costs and a strong premium on sawlog stumpage rates. Depending on economic circumstances, PCT and VM could be attractive investments on high quality sites that are situated in close proximity to wood processing infrastructure.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".