Amount of downed woody debris and its prediction using stand characteristics in boreal and mixedwood forests of Ontario, Canada
Bibliographic record
Abstract
We analyzed data on downed woody debris (DWD) from 435 permanent sample plots in boreal and mixedwood forests of Ontario seeking empirical relationships to predict DWD quantity from stand attributes. In each permanent sample plot, data were collected along three transects, including diameter, tree species, and degree of decomposition of DWD pieces with diameter greater than or equal to 7.5 cm at the point of intersection with the transect. Amounts of DWD in sample plots ranged from 0.7 to 402.7 m3·ha–1 and from 0.1 to 103.4 t·ha–1. Mean DWD values were 65.4 m3·ha–1 and 15.9 t·ha–1 in softwood- and 61.9 m3·ha–1 and 16.5 t·ha–1 in hardwood-dominated plots. Our analysis revealed no relationship between DWD and stand age, site index, or stocking for plots dominated by black spruce ( Picea mariana (Mill.) BSP), eastern white pine ( Pinus strobus L.), sugar maple ( Acer saccharum Marsh.), and red oak ( Quercus rubra L.) and weak relationships for plots dominated by jack pine ( Pinus banksiana Lamb.), red pine ( Pinus resinosa Ait.), trembling aspen ( Populus tremuloides Michx.), and white birch ( Betula papyrifera Marsh.). We submit that DWD in Ontario’s forests should be treated as a constant factor until the relationship between the amount of DWD and present stand condition is better understood and discuss considerations for future studies on DWD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".