Evaluating harvest costs and profit of commercial thinnings in softwood stands in west-central Maine: A case study
Bibliographic record
Abstract
Precommercial thinning (PCT) is a common silvicultural treatment in the management of young conifer forests. The positive effects of PCT on tree growth are well documented, however, there have been few operational studies of thinning productivity associated with later harvests in such stands and associated cost comparison with high-density, small-diameter stands. In the winters of 2012/2013 and 2013/2014 a long-term herbicide and PCT study in west-central Maine was commercially thinned using cut-to-length (CTL) and whole-tree (WT) harvesting systems in PCT and non-PCT stands, respectively. Thinning prescriptions consisted of three nominal removal intensities (33%, 50%, and 66% of the standing softwood volume) in a randomized block design with three to four replications. Stand density, basal area, hardwood content, and removal intensity were not significant in explaining variation in harvester and feller-buncher productivity. An analysis of unit cost of production indicated that wood chip production using a WT system in non-PCT stands is less costly than the production of roundwood using a CTL system in PCT stands. Profit, however, is similar for products harvested by either system. Our conclusion is that the WT system used in the study is economically feasible to treat highdensity, small-diameter stands in a commercial thinning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".