Bibliographic record
Abstract
With increasing global wildfire severity, effective fire detection methods are essential to mitigate widespread environmental and health impacts. A recent solution to this phenomena is the application of ensemble machine learning methods, which combine several models to create a more effective one. However, this raises several questions, notably whether an ensemble method is more effective than an individual model or if increasing the number of constituent models leads to overfitting. This paper conducts an ablation study on the Mixture of Experts (MoE) approach for forest fire detection via satellite imagery across a Canadian dataset. The model (MoE6) constitutes all six state-of-the-art architectures, including InceptionNet, ResNet, Vision Transformer (ViT), AlexNet, VGG-Net, and a baseline CNN. Experts of the MoE6 will be systematically removed to form MoE4 and MoE2, which constitute only the top four and top two performing constituent models respectively. We hypothesize that the MoE ensemble approach will outperform any constituent model (two heads are better than one). Furthermore, among the MoE architectures, we hypothesize MoE2 as the top model as it comprehensively integrates characteristics from top model architectures while mitigating overfitting. However, the results show that the original MoE6 was the top performer, achieving a peak accuracy of 93.13\% and ROC-AUC of 0.9303. This work provides a promising solution for improving wildfire detection accuracy and response times, potentially reducing the devastation caused by wildfires globally.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".