Bibliographic record
Abstract
By using parcel model theory to construct a two-dimensional parameter space formed by low-level atmospheric stability and moisture, a simple framework on which to examine certain fire parcel properties associated with vertical column development is established. This framework is used to investigate if the Haines Index has some skill at predicting wildfire severity, where wildfire severity is assumed to be directly connected to vertical column development and the result of significant fire parcel ascent. By modeling the ascent of a moist, entraining fire parcel in four different background states—a 3 km deep boundary layer, a 2 km deep boundary layer, a 3 km deep boundary layer topped by an inversion layer, and a 2 km deep boundary layer topped by an inversion layer—the study shows that parcel properties that describe ascent and vertical column development are most significant when the boundary layer temperature lapse rate is near adiabatic and lower-level atmospheric humidity is relatively high. A shallower instead of a deeper boundary layer lowers parcel ascent, and an upper-level inversion lowers parcel ascent even further. This study shows that entraining fire parcel properties and magnitudes associated with significant ascent do not necessarily correspond to a Haines Index for a potential for high fire severity. The results suggest that the Haines Index may need to be refined or reformed depending on the stability and humidity in the boundary layer and vertical structure of the atmosphere. This study is a start to understanding the influence of the background state on fire parcel convection and an attempt to explain how the Haines Index works from an elementary but physical point of view.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".