Predicting long-term sapling dynamics and canopy recruitment in northern hardwood forests
Bibliographic record
Abstract
Prediction of forest composition and structure over multiple generations of trees is often hampered by limited data on understory tree dynamics and the highly variable process of canopy recruitment in forest openings. In this paper, we describe a model of sapling dynamics and overstory recruitment for CANOPY, a spatially explicit, crown-based, individual-tree model of gap dynamics. The model incorporates gap size as a predictor of sapling recruitment and height growth, and it mimics the processes of sapling release, gap capture, and lateral gap closure. Calibration data were derived from 12 data sets with a wide range of stand ages and disturbance history in northern hardwood stands in the Great Lakes region, USA. The model accounted for 30%–62% of the variation in sapling density, composition, and growth rates. Predicted effects of increasing gap size on growth rate were similar to observed trends. Growth equations that included gap size as an independent variable generally gave better predictions of sapling density, species composition, and growth rates than equations based on conventional plot-level competition metrics. Long-term, 1000-year simulations produced estimates of stand basal areas and tree density in each size class that are close to the mean observed values for old-growth stands in the region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".