Growth and mortality following partial cutting in a trembling aspen – conifer stand: results after 10 years
Bibliographic record
Abstract
We present the effects, 10 years after treatment, of different cutting intensities (removal of 0%, 35%, 50%, 65%, and 100% of basal area (BA)) on the response of residual trees in a 60-year-old mixed aspen–conifer stand in Quebec, Canada. While decennial mortality reached 170 stems·ha–1 under natural conditions (control, 24 m2·ha–1 BA), the removal of mature trembling aspen ( Populus tremuloides Michx.) trees reduced hardwood losses to 125, 105, and 0 stems·ha–1, respectively, in the 35%, 50%, and 65% cuttings, without affecting losses of coniferous species (≤25 stems·ha–1). The growth response of conifers was generally higher than that of broad-leaved species. For hardwoods, the decennial gains in merchantable BA decreased with respect to the control (3.2 m2·ha–1), to 1.2 m2·ha–1 in the 35% cut and to 0.9 m2·ha–1 in the 50% cut, but increased under the 65% cut (2.5 m2·ha–1). For conifers, gains were proportional to removal in partial cuts (2.6–4.9 m2·ha–1) and lower in the control (1.7 m2·ha–1). As a whole, our results suggest that partial cutting may be an effective means of increasing growth of the softwood proportion occupying the intermediate and suppressed strata in a mixed aspen-dominated stand.
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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) | 0.000 | 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".