Temporal changes in stem decay and dead and sound wood volumes in the northeastern Canadian boreal forest
Bibliographic record
Abstract
Yield tables used for stand-level predictions of standing volume typically do not account for the presence of dead trees and stem decay. Yet, recently dead trees, referred to as dead and sound wood (DSW), could be considered as a valuable supplemental wood source. Conversely, stem decay can cause important losses during product recovery. Accordingly, the general objective of this study was to characterize the patterns of change of stem decay and of DSW as functions of time since the last fire (TSF). The amount of stem decay and of DSW per tree species were measured in two chronosequences of 30 stands each, covering more than 1000 years in the northeastern Canadian boreal forest. Stand-level decay volume increased during the first 150 years following fire and then stabilized. This volume was mainly composed of black spruce (Picea mariana (Mill.) BSP) when TSF <200 years and of balsam fir (Abies balsamea (L.) Mill.) when TSF >200 years. Conversely, the volume of DSW declined rapidly after fire and increased gradually from about 200 years TSF. Hence, the loss of wood volume attributable to stem decay in old-growth stands was cancelled out by the increased availability of DSW, with a slightly positive balance of 3.5 m3/ha. This could be significant considering the large amount of old-growth stands in this part of the boreal forest.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".