How Wildfire Intensity Impacts the Extent and Speed of Vegetation Succession: Old Fire versus Cedar Fire – California, USA 2003
Bibliographic record
Abstract
Wildfires are one of the most devastating natural disasters, with the ability and potential to destroy infrastructure and ecosystems. On October 25, 2003, two wildfires broke out in California, USA: Old Fire in San Bernardino County and Cedar Fire in San Diego County. These wildfires significantly damaged vegetation and are particularly interesting as they both started on the same day, burned for over a week, and were only approximately 150 kilometres apart, allowing for great comparisons to be made. This project determines which fire was more intense and how their intensities impacted the extent and speed of vegetation succession. Wildfire intensity refers to the amount of energy released by the fire. It incorporates components like ground temperature, flame height, and spread rate, and is influenced by factors such as weather conditions, plant chemistry, and topography. This project, however, observes weather conditions and topography specifically to reveal how these wildfires of different intensities impacted vegetation succession. LANDSAT satellite imagery in the shortwave infrared (SWIR) and thermal layers were analyzed using ENVI to observe fire size. Climate data provided the weather conditions before and during the fires. Government reports and news articles gave information on flame heights and spread rates. These contributed to determining fire intensity. Then, LANDSAT imagery in the Normalized Difference Vegetation Index (NDVI) layer was analyzed using ENVI over approximately twenty years (2003-2023) to observe the growing vegetation. While both fires were active for a similar duration, Cedar Fire was more intense, resulting in slower vegetation succession due to hotter ground temperatures, taller flame height, and a faster spread rate compared to Old Fire. The impact that both wildfires had on vegetation highlights the importance of mitigating future wildfire risk, especially as natural disasters like wildfires become more frequent and more intense with continuing global warming and climate change.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".