Death from hunger or thirst? Phloem death, rather than xylem hydraulic failure, as a driver of fire‐induced conifer mortality
Bibliographic record
Abstract
Disruption of photosynthesis and carbon transport due to damage to the tree crown and stem cambial cells, respectively, can cause tree mortality. It has recently been proposed that fire-induced dysfunction of xylem plays an important role in tree mortality. Here, we simultaneously tested the impact of a lethal fire dose on nonstructural carbohydrates (NSCs) and xylem hydraulics in Pinus ponderosa saplings. Saplings were burned with a known lethal fire dose. Nonstructural carbohydrates were assessed in needles, main stems, roots and whole plants, and xylem hydraulic conductivity was measured in the main stems up to 29 d postfire. Photosynthesis and whole plant NSCs declined postfire. Additionally, all burned saplings showed 100% phloem/cambium necrosis, and roots of burned saplings had reduced NSCs compared to unburned and defoliated saplings. We further show that, contrary to patterns observed with NSCs, water transport was unchanged by fire and there was no evidence of xylem deformation in saplings that experienced a lethal dose of heat from fire. We conclude that phloem and cambium mortality, and not hydraulic failure, were probably the causes of death in these saplings. These findings advance our understanding of the physiological response to fire-induced injuries in conifer trees.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".