Factors affecting fall down rates of dead aspen (<i>Populus tremuloides</i>) biomass following severe drought in west‐central Canada
Bibliographic record
Abstract
Increases in mortality of trembling aspen (Populus tremuloides Michx.) have been recorded across large areas of western North America following recent periods of exceptionally severe drought. The resultant increase in standing, dead tree biomass represents a significant potential source of carbon emissions to the atmosphere, but the timing of emissions is partially driven by dead-wood dynamics which include the fall down and breakage of dead aspen stems. The rate at which dead trees fall to the ground also strongly influences the period over which forest dieback episodes can be detected by aerial surveys or satellite remote sensing observations. Over a 12-year period (2000-2012), we monitored the annual status of 1010 aspen trees that died during and following a severe regional drought within 25 study areas across west-central Canada. Observations of stem fall down and breakage (snapping) were used to estimate woody biomass transfer from standing to downed dead wood as a function of years since tree death. For the region as a whole, we estimated that >80% of standing dead aspen biomass had fallen after 10 years. Overall, the rate of fall down was minimal during the year following stem death, but thereafter fall rates followed a negative exponential equation with k = 0.20 per year. However, there was high between-site variation in the rate of fall down (k = 0.08-0.37 per year). The analysis showed that fall down rates were positively correlated with stand age, site windiness, and the incidence of decay fungi (Phellinus tremulae (Bond.) Bond. and Boris.) and wood-boring insects. These factors are thus likely to influence the rate of carbon emissions from dead trees following periods of climate-related forest die-off episodes.
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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) | 0.001 | 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".