Bibliographic record
Abstract
Heat injuries sustained in a fire can initiate a cascade of complex mechanisms that affect the physiology of trees after fires. Uncovering the exact physiological mechanisms and relating specific injuries to whole-plant and ecosystem functioning is the focus of intense current research. Recent studies have made critical steps forward in our understanding of tree physiological processes after fires, and have suggested mechanisms by which fire injuries may interact with disturbances such as drought, insects and pathogens. We outline a conceptual framework that unifies the involved processes, their interconnections, and possible feedbacks, and contextualizes these responses with existing hypotheses for disturbance effects on plants and ecosystems. By focusing on carbon and water as currencies of plant functioning, we demonstrate fire-induced cambium/phloem necrosis and xylem damage to be main disturbance effects. The resulting carbon starvation and hydraulic dysfunction are linked with drought and insect impacts. Evaluating the precise process relationships will be crucial for fully understanding how fires can affect tree functionality, and will help improve fire risk assessment and mortality model predictions. Especially considering future climate-driven increases in fire frequency and intensity, knowledge of the physiological tree responses is important to better estimate postfire ecosystem dynamics and interactions with climate disturbances.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".