Probabilities of small-gap capture by sugar maple saplings based on height and crown growth data from felled trees
Bibliographic record
Abstract
We simulated the probability that Acer saccharum Marsh. saplings in single-tree gaps would reach the overstory before lateral gap closure. The model was calibrated with height and crown growth data from destructively sampled trees that ranged from 1 to 27 m tall. Each of the major initial conditions and growth processes was evaluated separately to determine its effect on gap-capture probabilities. Factors such as sapling height at the time of gap formation, continued height growth of border trees, and stochastic growth variation had pronounced effects on the outcome. Stochastic variation generally increased chances of sapling success by delaying closure times in some of the gaps and allowing some saplings to grow at above-average rates. In stochastic simulations with continued (asymptotic) border-tree height growth, probabilities of successful gap capture ranged from <20% of saplings 12 m tall to 35%86% for saplings 78 m tall. The results suggest that some saplings may be able to capture gaps after one gap event, but probabilities are low for small saplings and for all saplings in small and medium gaps. Based on the mechanisms simulated here, most of the larger single-tree gaps (78 m2) are captured by advance regeneration more than 4 m tall.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".