Understanding compositional stability in mixedwood forests of eastern North America
Bibliographic record
Abstract
Mixedwood forest composition, or co-dominance of hardwood and softwood species, has been interpreted as both stable and unstable. Through review of existing theory, we propose a conceptual model to understand mixedwood compositional stability in boreal and temperate forests of eastern North America. We first review the current theory that the strength of neighborhood effects (i.e., species ability to self-replace under their own canopy) is essential to understanding stability such that when self-replacement is strong for both dominant hardwood and softwood species, composition is stable except at extreme disturbance severities. In contrast, when mixedwood forests are dominated by negligible or weak affinities to self-replace, composition is unstable and sensitive to changes in disturbance. Our new concept further posits that changes in both the disturbance severity and its vertical direction are essential to understanding stability. For example, where moderate-severity surface fires (which impact forests from below) cease and are replaced by moderate-severity blowdowns (which impact forests from above), instability can occur even when disturbance severity is unchanged. We therefore pose and discuss an extension to current theory to provide a new unifying concept of stability for mixedwood forests and, more broadly, for mixed-species 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".