Partial cuts in a trembling aspen conifer stand: effects on microenvironmental conditions and regeneration dynamics
Bibliographic record
Abstract
We present the 5-year combined effects of different cutting intensities (removal of 0, 35, 50, 65, and 100% of basal area) and scarification on available light, soil temperature, and regeneration dynamics in a mixed aspen conifer stand in Quebec, Canada. Compared with the control, the 35% cut did not change transmitted light to the under story (<20% of full light), while the 50, 65, and 100% cuttings transmitted 30, 48, and 90% of full light, respectively, during the first summer. Trembling aspen (Populus tremuloides Michx.) suckering increased with percent basal area removal (p < 0.001). After 5 years, the 35 and 50% cuttings limited growth and survival of suckers (<1000 stems/ha, of which 5% are >1 m high), but the 65 and 100% cuttings favoured their development (8000 and 11 000 stems/ha, respectively, of which 29 and 38%, respectively, are >2 m high). Balsam fir (Abies balsamea (L.) Mill.) responded well to canopy opening alone with a maximal recruitment (31 000 seedlings/ha) in the 50% cut. Spruces (white spruce, Picea glauca (Moench) Voss, and red spruce, Picea rubens Sarg.) establish following scarification only, with a better response in the 65% cut (32 000 seedlings/ha) than in the 50 (15 000), 35 (10 000), and 0% (8000) cuttings.
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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".