Strategy for Fighting Wildfires Using Mean-Shift Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
To deal with the wildfires in Victoria, we need to arrange the drones reasonably. Taking into account capability, safety, economy and topography, we use Mean-Shift algorithm to determinate the optimal numbers and mix of drones and predict the situation of extreme wildfires in the future. Finally, we determine the optimal number and combination of drones after optimization. According to the size, frequency and locations of wildfires in Victoria in 2019, we use logistic model to estimate the general location of the wildfires. And we use Mean Shift algorithm to find the Optimal number, mix and locations of drones. The result is we need 95 SSA drones, 122 repeater drones and 122 drones with two functions, and the total cost is $3,390,000.
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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 | 0.000 | 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 it