A soil heat and water transfer model to predict belowground grass rhizome bud death in a grass fire
Bibliographic record
Abstract
Abstract Question:Grasses often resprout from surviving belowground buds following a fire in which aboveground matter is consumed. We used a soil heat and water transport model to present a general method for determining the potential mortality of rhizome buds due to fire for three tallgrass species (Andropogon gerardii, Sorghastrum nutans, andPanicum virgatum). Methods:Soil heating was described by physical processes that include heat conduction through the soil and heating and evaporation of soil water. We considered the following factors: soil moisture, texture, mineral thermal conductivity, maximum surface temperature, and fire residence time. Simulated soil temperature profiles were combined with measured belowground bud distributions to determine the proportion of buds expected to be heated to lethal temperatures under various conditions. Location:Wisconsin, USA. Results:Lethal temperatures for buds do not occur below ∼2 cm, and at least 30% of rhizome buds remain below lethal temperatures, even under extreme conditions. Conclusions:The model explains the possible mechanisms for grass belowground rhizome bud survival in fires. Changes in fire and soil conditions do not notably impact soil temperatures and rhizome bud survival.
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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 | 0.001 | 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".