Evaluating the long-term influence of alternative commercial thinning regimes and harvesting systems on projected net present value of precommercially thinned spruce–fir stands in northern Maine
Bibliographic record
Abstract
Commercial thinning (CT) is an important silvicultural practice in the northeastern United States. Relatively little is known, however, about the interaction of harvest system and treatment (removal intensity or timing of entry) on the overall profitability of CT. To address this question, 10-year measurements from a controlled CT experiment across six sites in Maine were used to project the long-term effect of removal intensity (33% and 50% relative density reduction) and timing of entry (no delay, 5-year delay, 10-year delay) on (i) maximum net present value (NPV), (ii) timing of maximum NPV, and (iii) the effect of three harvesting systems (cut-to-length, whole-tree, hybrid systems) on maximum NPV. A regional growth and yield model was used to project individual-tree growth and mortality into the future. Harvest costs for the harvesting systems were estimated using regional cycle-time equations. No difference was found in maximum NPV between the CT and non-CT areas or the timing of CT entry. Stand age at time of maximum NPV differed between delays but not between the two removal intensities. Our results indicate that there is no economic benefit in delaying a CT or removing more volume at the time of thinning for the range of stand conditions evaluated.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".