Temperature-dependent rate models of vascular cambium cell mortality
Bibliographic record
Abstract
We use two rate-process models to describe cell mortality at elevated temperatures as a means of understanding vascular cambium cell death during surface fires. In the models, cell death is caused by irreversible damage to cellular molecules that occurs at rates that increase exponentially with temperature. The models differ in whether cells show cumulative effects of heating. The temperature dependencies of the models' rate parameters were estimated from cell-count data after exposing live-bark tissues from four Canadian Rocky Mountain tree species to a range of fixed temperatures in a water bath. Based on both models, lodgepole pine's (Pinus contorta Dougl. ex Loud.) growing season vascular cambium cells experienced lower mortality rates at elevated temperatures than those of aspen (Populus tremuloides Michx.), Engelmann spruce (Picea engelmannii Parry ex Engelm.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Growing and dormant season differences were marginal. With reservations for lodgepole pine, both models predicted cell survival after exposures to rising and falling temperatures such as would be experienced by live tissues during fires. A simulation involving conduction heat transfer from flames and vascular cambium cell mortality suggests that differences among species in thermal tolerance are small compared with the effects of bark thickness. Although stem vascular cambium cell mortality was complete when tissues reached 60 °C during simulated surface fires, it may not be warranted to apply the 60 °C threshold to other tissues exposed to contrasting temperature regimes during fires.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".