Bibliographic record
Abstract
Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, my aim was to employ primarily remote sensing data in forecasting the forest fire danger conditions in the Canadian province of Alberta. Thus, I followed three specific objectives. Firstly, I generated topography-based static fire danger (SFD) map upon exploring the relationship between topographical elements (i.e., elevation, slope, and aspect) and fire occurrences. Since, the slope was found to be the best predictor for fire occurrences; I generated a slope-derived probability of forest fire occurrences. However, I did not incorporate the obtained map in the final specific objective as it had very small low fire danger areas. Secondly, I examined the possibility of lightning-caused fires modelling using remote sensing-derived vegetation moisture content in natural subregion level. I employed 8-day composite of normalized differences water index (NDWI) at 500 m spatial resolution along with historical lightning-caused fire occurrences during the 2005-2016 period. Employing the cumulative frequency cumulative-values of natural subregion-specific median NDWI and lightning-caused fire frequencies from snow disappearance date to the peak of the growing season, I found strong agreements (i.e., R2 ≥ 0.96) between these two frequencies for each of the subregions. Finally, I developed an advanced forest fire danger forecasting system upon applying three modifications on the exiting FFDFS, and incorporating the outcomes in the scope of the previous specific objectives. Then I examined the outcomes of the different combinations against the actual fire spots during the fire seasons of 2009–2011. Among all of the combinations, I found that the integration of modified FFDFS and human-caused SFD map demonstrated the most effective results in fire detection, i.e., about 82% on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. I strongly believe that my developments would be useful in the forest fire management.
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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 | 0.000 | 0.000 |
| 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 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".