Analysis of stand basal area development of thinned and unthinned <i>Acer rubrum</i> forests in the upper Great Lakes region, USA
Bibliographic record
Abstract
Red maple (Acer rubrum L.), historically a common but not abundant tree species in North America, has increased in abundance throughout its range over the last several decades; however, it has received little attention in growth and yield studies. The objectives of this study were to (i) evaluate the effects of stocking level and stand density on overall patterns of red maple stand productivity and (ii) quantify these relationships across a wide range of stand age, site quality, geographic location, and climatic conditions. We used long-term measurements from 52 sites in Wisconsin and Michigan to examine growth responses of even-aged red maple stands to various levels of thinning. Using linear, mixed-effects modeling, future stand-level red maple basal area was modeled as a function of stand and plot characteristics and climatic variables. Growing season precipitation and its interaction with initial red maple basal area were significant predictors; however, they only collectively reduced the mean squared error by 2.1% relative to a base model containing solely stand and plot factors. Model projections indicated there was little difference in predicted future basal area for the range of climate conditions experienced by these stands highlighting red maple’s wide tolerance of environmental conditions across 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 | 0.000 |
| 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 | 0.000 |
| 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".