Tree physiological responses after biotic and abiotic disturbances revealed by a dual isotope approach
Bibliographic record
Abstract
Tree mortality episodes, which change the forest stand structure, are induced by biotic or abiotic disturbances like pest outbreaks and pathogens attacks, fire, windthrow or climatic extreme events. While these processes are natural and forests mostly well adapted to them, there is some evidence of an acceleration of some disturbance processes due to climate change in the past decades (Seidl et al. 2017), and the predicted future temperature increase is expected to lead to further changes in forest dynamics and structure (McDowell et al. 2020). For example, an increase in global fire occurrence and severity has been documented, indicating a shift from a pre-industrial precipitation-driven to a future temperature-driven fire regime (Pechony and Shindell 2010). Also, fungal pathogens can cause large-scale forest disturbances, but their interaction with climate change is complex and not well understood. In Western North America, Douglas fir is an economically...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".