Initial Stand Volume and Residual Live Trees Drive Deadwood Carbon Stocks in Fire and Harvest Disturbed Boreal Forests at North‐Central Alberta
Bibliographic record
Abstract
ABSTRACT Retention forestry involves leaving single or groups of unharvested trees within harvest areas. Patch retention, which resembles structures such as unburned patches remaining after wildfire, is one practice implemented within the framework of Ecosystem‐based Forest Management (EBM), which seeks to use natural forests as a model and minimize differences in natural and managed forests. Despite the widespread adoption of patch retention practices, few comparisons of the attributes of postfire and postharvest islands, or their drivers, have been made. Given the importance of deadwood in forests to a variety of ecosystem functions, we sought to compare the local bioenvironmental drivers of deadwood (snags, CWD) C stocks in islands remnants in postfire and postharvest forests a decade after disturbance. We also determined whether their relative effects are consistent across deadwood types (snags, CWD) and disturbance regimes using generalized additive mixed models with study site as random factor in all cases. A candidate model with initial stand volume (ISV), basal area of live trees, and size heterogeneity of live trees best predicted snag and CWD C stocks in both disturbance types, but their relative importance was inconsistent. The ISV had significantly (p < 0.05) positive effects on C stocks in snags and CWD across disturbance types, but its relative effects was higher in retention islands than fire islands. In all cases, stand density of remnant live trees was negatively related to deadwood C stocks. Conversely, the size heterogeneity of remnant live trees significantly boosted deadwood C stocks in fire islands but not in harvest islands. The results imply consideration for the stocking level of candidate forest areas for retention patches as this drives the evolution of deadwood accumulation in the postharvest islands.
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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 | 0.001 |
| 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 | 0.001 |
| 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".