Rx-CADRE (Prescribed Fire Combustion-Atmospheric Dynamics Research Experiments) collaborative research in the core fire sciences
Bibliographic record
Abstract
The Rx-CADRE project was the combination of local and national fire expertise in the field of core fire research. The project brought together approximately 30 fire scientists from six geographic regions and seven diff erent agencies. The project objectives were to demonstrate the capacity for collaborative research by bringing together individuals and teams with a wide range of fire monitoring expertise and equipment from across the United States and Canada in order to instrument prescribed burns in the southeastern United States. A concurrent workshop was organized that brought fire and fire effects modelers into the mix, creating the linkage between data generation and data use for fire and fire effects model validation and development. The group documented fire-atmospheric dynamics on prescribed fires in southern pine (Pinus palustris) woodlands of varying size, ranging from 10 to 1,000 ha. Specifically, we 1) compared in situ and remote-sensed heat environments of prescribed burns, 2) documented coupled atmospheric interactions, 3) produced validation data sets for coupled fire-atmospheric dynamic models, and 4) related fire behavior to first-order fire effects. On the fully instrumented fires, the group collected data on pre-burn fuel loads, post-burn consumption, ambient weather, in situ convective dynamics, plume dynamics, radiant heat release (both from in situ and remote sensors), in situ fire behavior, and select fire effects.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".