Modeling tolerant hardwood sapling density and occurrence probability in the Acadian forests of New Brunswick, Canada: Results 14 years after harvesting
Bibliographic record
Abstract
Natural forest regeneration after natural or anthropogenic disturbance is difficult to predict given its high variability. The process is poorly documented for commercial northern hardwood species in the Acadian forest of eastern Canada. Our objective was to identify the silvicultural, environmental, and ecological factors that best explain the variability in sapling density and occurrence of two commercial tolerant hardwood species in New Brunswick: American beech (Fagus grandifolia Ehrh.) and sugar maple (Acer saccharum Marsh.). Forty-three permanent sample plots were established in 2002 and measured before harvesting in 2004. Sapling density and occurrence were measured 14 years after harvesting. The results showed that the interactions between the species and the residual merchantable basal area and between the species and the percent of hardwoods in the original stand best explained the sapling density and occurrence variation of tolerant hardwoods. The sapling density of sugar maple increased with increasing merchantable residual basal area. However, the effect of this variable was not significant for the density of American beech saplings. The density and occurrence of tolerant hardwood saplings both increased along with the percent of hardwoods in the original stand. These results provide an improved understanding about tolerant hardwood regeneration dynamics in New Brunswick forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".