The influence of fuelbed properties on moisture drying rates and timelags of longleaf pine litter
Bibliographic record
Abstract
Fire managers often model pine needles as 1 h timelag fuels, but fuelbed properties may significantly change the rate at which needles exchange moisture with the atmosphere. The problem of determining whether moisture loss from fine fuels is being controlled by individual particles or by the fuelbed remains unresolved. Results from this laboratory experiment indicate that first-period timelags of longleaf pine ( Pinus palustris Mill.) needles are altered by fuelbed loading and needle arrangement. Timelags of individual needles ranged from 3.3 to 5.3 h; timelags of beds of vertically oriented needles (4.4 to 8.6 h) approximated those of individual particles, but were slightly influenced by loading. Beds of horizontal needles dried with load-dependent timelags that varied from 6.5 to 31.6 h. Fuel loads ranged from 0.04 (for individual particles) to 1.07 kg·m–2. We report a new metric, the area drying rate, which is analogous to a unit-area burning rate. For beds of flat needles, plots of the area drying rate versus fuel load illustrate a transition from control by individual particles to control by the bed structure when fuel loading is approximately 0.33 kg·m–2. Beds of vertical needles were particle controlled. Results should be useful to fire managers when modeling fire behavior.
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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 | 0.001 |
| 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) | 0.000 | 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".