Time Series Classification and Augmentation with Domain-agnostic Self-supervised Learning for Combustion Signature Analysis
Bibliographic record
Abstract
When responding to freight transportation fire incidents with unknown or mixed materials, first responders require decision support on the appropriate emergency response strategy. In this work, we propose to use machine learning (ML) to aid first responders by identifying hazard characteristics (e.g., toxicity and explosivity) of a given fire using the time series profile of chemicals found in the effluent. We experiment with time series classification algorithms, enhance their performance with time series augmentation techniques, and demonstrate that a model named MultiRocket achieved high testing performance with minimal inference time. Additionally, we explore using self-supervised learning to learn patterns of the data without the need of any handcrafted augmentations and demonstrate that it is a promising alternative avenue to training supervised ML models. Our proposed toolkit benefiting from machine learning will pave the way for further research in accessible, easily-deployable AI-enabled tools in fire sciences and 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".