Patterns of leaf area index during stand development in even-aged balsam fir – red spruce stands
Bibliographic record
Abstract
Leaf area index (LAI) strongly controls forest stand production. Silviculturists can easily manage this biologically important variable by quantifying its relationship to more directly manageable stand elements, such as density. Hypothesized patterns of LAI development over relative stand density (RD) in even-aged stands of balsam fir ( Abies balsamea (L.) Mill.) and red spruce ( Picea rubens Sarg.) were examined using 78 plots from the Cooperative Forestry Research Unit’s Commercial Thinning Research Network located in the Acadian forest zone in Maine. Nonlinear regression indicated that LAI was significantly related to RD, site quality, and stand top height. LAI increased nonlinearly with increasing RD holding stand top height constant. At a given RD, LAI peaked at approximately 13 m in stand top height. Site quality positively and linearly influenced LAI, but this was only apparent after crown closure, which in turn was influenced by initial stand density. Five-year trajectories of LAI–RD remeasurement data showed an increase in LAI and RD for all stands that varied by stand top height and site quality. Taken together, RD, stand top height, and site quality are strong predictors of LAI and can be used by silviculturists to manage for LAI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".