Coarse woody debris: Inventory, decay modelling, and management implications in three biogeoclimatic zones
Bibliographic record
Abstract
To assess recent management practices, post-harvest levels of coarse woody debris (CWD) were measured in the Southern Interior and Northern Interior forest regions of British Columbia. A simple input and decay model was used to estimate the volumes of CWD that might be present at the end of managed forest rotations. In four ecosystems (Sub-Boreal Spruce [SBS] mk1 variant, Interior Douglas-fir [IDF] dm2 variant, Interior Cedar–Hemlock [ICH] dw variant, and ICHvk2/wk3 variants) that were sampled a few years after harvest, between 58 and 80% of the CWD volume came from pieces less than 6 m in length. Modelling of CWD decay and net new CWD input from the developing stand indicated that by rotation end (after 90 years), CWD volumes would have decreased to about 15% (SBSmk1) and 1% (IDFdm2) of the CWD volumes found in mature unmanaged stands.In the ecosystems studied, this research suggests that specific management guidance for deadwood will be required to maintain CWD (outside of reserves) in managed stands. Various techniques could be employed to manage the CWD resource. The purpose of this paper is not to present such techniques; however, the sampling and modelling methodology outlined here will help to formulate management approaches by allowing an assessment of CWD presence throughout a managed forest rotation.
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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 | 0.001 | 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".