American beech sapling dominance: Stand-scale insights from mobile laser scanning
Bibliographic record
Abstract
For several decades, the proportion of American beech (Fagus grandifolia) has increased in sugar maple (Acer saccharum) stands, particularly in the understory, where it can hinder the regeneration of other species. Although this dominance is well documented, the underlying mechanisms by which it develops remain poorly understood. We aimed to determine whether the spatial relationships between beech saplings, mature beech trees, and canopy openness, previously observed among stands using discontinuous plot-based sampling, also applies within stands using continuous sampling of full stands. Our objective was to better understand how this dominance develops, as it is uncertain whether these relationships hold at finer spatial scales within stands, i.e., whether beech dominance within a stand is spatially related to canopy gaps and/or mature beech density. We created 3D-maps of 11 1-ha stands using mobile laser scanning (MLS) technology and developed a new method for analyzing regeneration which allowed us to map a total of 8455 trees and 30 498 saplings and to investigate the relationship between beech saplings and mature beech trees as well as canopy openness at various spatial scales. Contrary to our expectations, we found very little to no relationship between beech saplings and canopy openness, and a highly variable relationship with mature beech trees amongst sites. Our findings also underscore the importance of spatial scale in regeneration studies and suggest that the phenomenon of beech proliferation is likely ineluctable, as sexual reproduction appears to play a more important role in beech regeneration than previously thought.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".