Growth rate predicts mortality of <i>Abies concolor</i> in both burned and unburned stands
Bibliographic record
Abstract
Tree mortality is often the result of both long-term and short-term stress. Growth rate, an indicator of long-term stress, is often used to estimate probability of death in unburned stands. In contrast, probability of death in burned stands is modeled as a function of short-term disturbance severity. We sought to narrow this conceptual gap by determining (i) whether growth rate, in addition to crown scorch, is a predictor of mortality in burned stands and (ii) whether a single, simple model could predict tree death in both burned and unburned stands. Observations of 2622 unburned and 688 burned Abies concolor (Gord. & Glend.) Lindl. (white fir) in the Sierra Nevada of California, U.S.A., indicated that growth rate was a significant predictor of mortality in the unburned stands, while both crown scorch and radial growth were significant predictors of mortality in the burned stands. Applying the burned stand model to unburned stands resulted in an overestimation of the unburned stand mortality rate. While failing to create a general model of tree death for A. concolor, our findings underscore the idea that similar processes may affect mortality in disturbed and undisturbed stands.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".