Bibliographic record
Abstract
After analyzing the background and requirements of fighting wildfires, we decide to set two models, linear programming model and grey prediction model.We start the analysis strictly from the actual situation. Since the number of drones depends on the distance between the EOC and the fire points, we creatively transform the problem of the optimal number of drones into the evaluation problem of EOC site selection. We use the sum of the distances between the EOC and various ignition points as the objective function to establish a linear programming model. Then we summarized the fire situation into three typical periods according to the actual situation, comprehensively considering the characteristics of fire incidents, the number and location of EOC, to balance ability, safety and economy. Through the immune optimization algorithm (Immune Algorithm), the problem of best address is solved, and then the number of drones required is calculated, and the best solution for the Victoria fire incident is analyzed. We not only give the number of SSAs and UAVs carrying repeaters required in various situations, but also give an accurate and optimal distribution of UAV positions through a large number of computer simulations. This shows our determination and efforts to balance economy and safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".