Elevated mortality of residual trees following structural retention harvesting in boreal mixedwoods
Bibliographic record
Abstract
In recent years boreal forests have been harvested to retain a portion of the original canopy, thereby providing forest structure, mostly for biodiversity reasons. Boreal mixedwood cutovers were surveyed at one and five years after harvesting with approximately 10% structural retention, to quantify the mean annual mortality rates of the residual trembling aspen, balsam poplar, paper birch and white spruce trees. For comparison, "natural" mortality rates by species were estimated from permanent sample plots in stands of similar composition. Species ranking of the annual mortality rates of residuals in areas harvested with structural retention were: poplar (10.2%) > birch (8.7%) > aspen (6.1%) > spruce (2.9%). Annual mortality rates were 2.5 to 4 times greater than in the reference stands. The majority of broadleaved species died as snags (~70%–90%), while most spruce died due to windthrow (80%). Mortality rates increased with slenderness coefficient for codominant and understory poplar and for understory birch. For aspen, codominants were most likely to die, while in spruce, dominant trees and trees with the greatest damage to the bole from harvesting operations had the highest mortality. Key words: Alberta, Betula papyrifera, dieback, harvesting damage, mixedwood forests, variable retention, Picea glauca, Populus balsamifera, Populus tremuloides, structural retention, sustainable forest management
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".