Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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