Silviculture affects composition, growth, and yield in mixed northern conifers: 40-year results from the Penobscot Experimental Forest
Bibliographic record
Abstract
This long-term experiment in Maine, U.S.A., was designed to provide information on the best silvicultural practices for managing stands of mixed northern conifers in northeastern U.S.A. We evaluated growth and yield and changes in species composition, quality, and structure during the first 40 years of the experiment. Replicated treatments include the selection system, uniform shelterwood, unregulated harvesting, and diameter-limit cutting. The new cohort established under three-stage shelterwood was subsequently left untreated or precommercially thinned. Between-treatment differences in net volume growth were not significant (α = 0.10), though gross volume growth differed significantly for managed vs. unmanaged, selection vs. shelterwood, and shelterwood vs. diameter-limit treatments. A three-stage shelterwood method with precommercial thinning 10 years following final overstory removal resulted in good control of hardwoods and hemlock and a dramatic increase in spruce and fir. The selection system on a 5-year cutting cycle resulted in increased hemlock, spruce, and fir, with a decrease in hardwood species. If the primary goal were production, even-aged management would most likely be preferred. We recommend a two-stage shelterwood method as applied in this experiment with some modification to improve species composition and stand quality. Stand quality (proportion of stand volume in cull trees) and species composition was influenced by treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".