Bibliographic record
Abstract
Abstract Question: Can the direct regeneration hypothesis (DRH) be used to predict post‐disturbance regeneration after fire, wind disturbance, and clearcutting in northern forests? Do life‐history traits such as regeneration strategy and shade tolerance influence post‐disturbance regeneration success of tree species? Location: Northern forests in North America. Methods: A meta‐analysis was conducted by collecting published data on pre‐ and post‐disturbance stand compositional characteristics in the northern forests. For each tree species, compositional difference (CD) was calculated as the difference between basal area proportions of the post‐ and pre‐disturbance stands, but for post‐disturbance stands <25 years of age, post‐disturbance proportions were calculated based on relative stem density. Results: Species response to disturbances was best explained by regeneration strategy, while disturbance type had no effect on CD. The proportion of broadleaf trees with either strong or weak vegetative reproduction ability increased after all disturbances. Serotinous species had CD values not significantly different from zero after fire, while CD for semi‐serotinous species was negative. The post‐disturbance proportions of non‐serotinous conifers decreased after all forms of disturbance. Conclusions: All disturbances promote broadleaf trees, regardless of regeneration strategy (suckering, sprouting, or seeding). The DRH is supported for conifers with serotinous cones after fire. Fire causes local extinction of non‐serotinous conifers, while wind and clearcutting only decrease the proportion of non‐serotinous conifers because of partial survival of seed sources and advanced regeneration. This study suggests that increasing stand‐replacing disturbances associated with global climate change will promote broadleaf trees in northern 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".