Regeneration dynamics after patch cutting and scarification in yellow birch – conifer stands
Bibliographic record
Abstract
We present the 6 year effects of different cutting patterns (patch-selection cutting with 20, 30, and 40 m diameter gaps, 1 ha patch clear-cut, and uncut control) and spot scarification, on seedbed coverage and regeneration dynamics in yellow birch ( Betula alleghaniensis Britton) – conifer stands in eastern Quebec, Canada. After 3 years, yellow birch had established better in cutting patterns with gaps than in the patch clear-cut and in the control, while its density was 7 times higher in scarified than in nonscarified subplots. After 6 years, scarified openings and the borders of openings had 3–5 times more seedlings >30 cm in height than nonscarified openings and the understory between the gaps. The loss of advance growth in openings was the main result for conifer species, although recruitment of new balsam fir ( Abies balsamea (L.) Mill.) seedlings was accelerated by scarification. Despite the abundance of red spruce ( Picea rubens Sarg.) seed-trees on the site, our treatment combinations failed to promote its natural regeneration. Varying gap size did not change the total density of competing vegetation but modified the composition of this shrub layer. Our 6 year results suggest that maintaining conifer species, and the mixed composition of the stands, is uncertain over the long term.
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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".