Crop tree growth response and quality after silvicultural rehabilitation of cutover stands
Bibliographic record
Abstract
Rehabilitation of cutover stands is often a management objective of landowners who desire improved stand conditions and increased value from future harvest revenues. We evaluated crop tree growth response and quality following precommercial rehabilitation treatments in mixedwood stands degraded through repeated exploitive cutting in Maine, USA. Treatments included control (no rehabilitation), moderate rehabilitation (crop tree release), and intensive rehabilitation (crop tree release plus timber stand improvement). Paper birch (Betula papyrifera Marsh.), red spruce (Picea rubens Sarg.), and eastern hemlock (Tsuga canadensis (L.) Carriere) crop tree diameter increments 0 to 9 years after treatment were greater following rehabilitation than in the control. Diameter increment did not differ between intensities of rehabilitation for any species. For conifers in the lower strata, crop tree height growth and change in crown length were negatively correlated with basal area in larger trees. The occurrence of epicormic branches on paper birches was greater in the rehabilitation treatments than the control. However, most epicormic branches occurred above the height corresponding to the first sawlog. These findings indicate that rehabilitation of mixedwood stands with similar characteristics can result in improved growth of crop trees without jeopardizing the quality of the lower bole in paper birches.
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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 | 0.000 | 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) | 0.001 | 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".