The relationship of stand structure with canopy transmittance: Simple models and practical methods for managing understory light conditions in eastern white pine (<i>Pinus strobus</i> L.)-dominated forests
Bibliographic record
Abstract
The relationship of stand structural features with understory light levels, estimated by gap light index (GLI), was investigated in 22 second-growth eastern white (Pinus strobus L.) and red pine (Pinus resinosa Ait.)-dominated stands in central Ontario that encompassed a broad range in density and basal area. Simple, empirical light models were developed to quantify the influence of several stand structural variables on canopy transmittance as estimated by GLI. Models were also derived to facilitate the operational identification of residual basal area, density, and percent canopy closure associated with target understory light levels that optimize the growth of white pine regeneration and its protection from weevil and blister rust when using the uniform shelterwood silvicultural system. Regression models indicated significant negative, nonlinear relationships of GLI with density, basal area, a stand density index, total crown area, and foliar biomass, while GLI was linearly related to percent canopy closure. Application of these models to identify density, basal area, and canopy closure values associated with target light levels for the regeneration and removal cuts of uniform shelterwoods demonstrates the use of this information to help guide management of white pine–red pine forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".