Improving tree mortality models by accounting for environmental influences
Bibliographic record
Abstract
Tree-ring chronologies have been widely used in studies of tree mortality where variables of recent growth act as an indicator of tree physiological vigour. Comparing recent radial growth of live and dead trees thus allows estimating probabilities of tree mortality. Sampling of mature dead trees usually provides death-year distributions that may span over years or decades. Recent growth of dead trees (prior to death) is then computed during a number of periods, whereas recent growth (prior to sampling) for live trees is computed for identical periods. Because recent growth of live and dead trees is then computed for different periods, external factors such as disturbance or climate may influence growth rates and, thus, mortality probability estimations. To counteract this problem, we propose the truncating of live-growth series to obtain similar frequency distributions of the “last year of growth” for the populations of live and dead trees. In this paper, we use different growth scenarios from several tree species, from several geographic sources, and from trees with different growth patterns to evaluate the impact of truncating on predictor variables and their selection in logistic regression analysis. Also, we assess the ability of the resulting models to accurately predict the status of trees through internal and external validation. Our results suggest that the truncating of live-growth series helps decrease the influence of external factors on growth comparisons. By doing so, it reinforces the growth–vigour link of the mortality model and enhances the model’s accuracy as well as its general applicability. Hence, if model parameters are to be integrated in simulation models of greater geographical extent, truncating may be used to increase model robustness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".