Reconstructing the competitive dynamics of mixed-oak neighborhoods
Bibliographic record
Abstract
The disparity between the potential for latent oak dominance within a stand and their region-wide decline in importance value raises questions about the competitiveness of oaks in early stand dynamics. We reconstructed tree height growth dynamics in mixed-species neighborhoods to determine if currently dominant oaks were ever shorter than their competitors and at what age currently subordinate oaks fell behind. In 23–36 year old mixed-oak stands in two physiographic provinces of Pennsylvania, we identified dominant and subordinate northern red oaks ( Quercus rubra L.) and chestnut oaks ( Quercus prinus L.) competing with adjacent non-oaks (red maple ( Acer rubrum L.), sweet birch ( Betula lenta L.), and black cherry ( Prunus serotina Ehrh.)) of equal crown class. In each stand, we randomly selected 20 tree neighborhoods and collected stem cross sections every metre from the base to the tip of each tree. In the Allegheny Plateau province, dominant northern red oaks never averaged more than 2 m shorter than their competitors, while by age 20, subordinate oaks were commonly more than 2 m behind. Dominant chestnut oaks in the Ridge and Valley province were never more than 1 m shorter than their competitors; subordinate oaks, however, were generally always at least a metre behind. In both regions, growth dynamics of currently subordinate trees were indistinguishable from those of currently dominant trees during their first decade. Because oaks that were considerably behind at age 15 were likely to be subordinate by age 30, the window of opportunity for release to prevent oaks from becoming permanently overtopped and unable to recover may thus be relatively early (age 10–20).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".