Partitioning risks of tree mortality by modes of death in managed and unmanaged northern hardwoods and mixedwoods
Bibliographic record
Abstract
We used a permanent sample plot network established to monitor the effects of forest management practiced in mature northern hardwood and mixedwood stands in Québec, Canada to address two important questions related to tree mortality: (1) How does partial cutting affect tree mortality? (2) Which variables best explain tree mortality by different modes of death (standing death, uprooting or stem breakage)? Decennial tree deaths were lower after partial cutting than in unharvested plots, but the annual proportion of tree death was similar (1.25%·year-1). At the tree level, a risk-product classification demonstrated the strongest evidence on influencing total mortality, followed by species, the presence of a logging injury, and tree size. Annual temperature was the only climatic variable explaining variations in total risk of tree mortality, and always among the first three variables explaining differences among the three modes of death. The risk of standing death also increased with increasing annual precipitation, while the risk of stem breakage and, to a lesser extent, of being uprooted, decreased with increasing precipitation. Additionally, we found strong evidence to support the effect of the potassium/magnesium ratio of the humus layer on the risk of being uprooted. This information is critical to better understand tree mortality processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".