Decennial growth and mortality following uniform partial cutting in yellow birch – conifer stands
Bibliographic record
Abstract
Estimating residual tree survival and growth is crucial for evaluating the overall merit of partial harvesting. In this case study, we present the effects of different cutting intensities (0%, 40%, 50%, and 60% of merchantable (diameter at breast height ≥ 9.1 cm) basal area (BA)) on the response of residual trees in two mixed yellow birch (Betula alleghaniensis Britt.) – conifer stands in eastern Quebec, Canada. Primarily aimed at promoting regeneration establishment, the experiment was conducted in two sites 90 km apart (Armagh and Duchesnay), each one containing four replicates of treatments in a randomized block design. Mortality after cutting decreased with increasing BA removal, but losses were two to three times higher at Armagh (62–138 stems/ha) than at Duchesnay (22–88 stems/ha). Loss of conifer stems involved primarily balsam fir (Abies balsamea (L.) Mill.) under natural conditions (control), whereas fir and red spruce (Picea rubens Sarg.) were equally affected in partial cuts. Red maple (Acer rubrum L.) and paper birch (Betula papyrifera Marsh.) were lost regardless of treatment. As a whole, growth in merchantable BA increased with cutting intensity. Uniform partial cuts produced good BA growth response from conifers at Armagh (0.27–0.28 m2·ha−1·year−1) and from hardwoods at Duchesnay (0.16–0.25 m2·ha−1·year−1), whereas BA growth was negligible for both species groups in the control. We examine the role of species composition and stand structure before cutting in the response of residual trees.
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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) | 0.000 | 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".