Tolerant hardwood natural regeneration 15 years after various silvicultural treatments on an industrial freehold of northwestern New Brunswick
Bibliographic record
Abstract
We examined applicability of various partial cutting systems in order to regenerate tolerant hardwood stands dominated by sugar maple (Acer saccarhum), American beech (Fagus grandifolia) and yellow birch (Betula alleghaniensis) on northern New Brunswick J.D. Irving Ltd. freehold land. Sampling of 1065 one-m2plots in 31 stands managed by selection cutting, shelterwood method and strip or patch cutting and in six control stands allowed a 15-year retrospective study of natural regeneration in stands of low residual densities and with minimal soil disturbance and no control of competing vegetation. Beech regeneration was most abundant in the patch cuts, yellow birch in shelterwood stands and sugar maple in the selection system areas. Results suggest that initial stand conditions influence the composition of the regeneration more than the prescribed treatment. At the stand scale (a few hectares), sugar maple recruitment was positively influenced by its proportion in the initial stand, and negatively by the cover of herbs and shrubs. Yellow birch regeneration was mainly affected by shrub competition. At the plot (1 m2) scale, mineral soil and decayed wood substrates and ground-level transmitted light were determinant factors for yellow birch regeneration. Beech-dominated stands were likely to regenerate to beech. A dense beech sucker understory was promoted in harvested patches. Areas with dense understory of American beech, shrubs, or herbs require site preparation to reduce interference either before or at the time of partial cutting. Shelterwood seed cutting and selection cutting should leave a residual of 12 m2/ha and 17 m2/ha respectively in seed trees uniformly distributed.
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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".