Stand- and landscape-level effects of prescribed burning on two Arizona wildfires
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performance of fuel treatments in modifying behavior and effects of the largest wildfires has rarely been evaluated, because the necessary data on fire movement, treatment characteristics, and fire severity were not obtainable together. Here we analyzed satellite imagery and prescribed fire records from two Arizona wildfires that occurred in 2002, finding that prescribed fire treatments reduced wildfire severity and changed its progress. Prescribed burning in ponderosa pine forests 19 years before the Rodeo and Chediski fires reduced fire severity compared with untreated areas, despite the unprecedented 1860-km 2 combined wildfire sizes and record drought. Fire severity increased with time since treatment but decreased with unit size and number of repeated prescribed burn treatments. Fire progression captured by Landsat 7 enhanced thematic mapper plus (ETM+) clearly showed the fire circumventing treatment units and protecting areas on their lee side. This evidence is consistent with model predictions that suggest wildland fire size and severity can be mitigated by strategic placement of treatments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| 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 it