Estimating air methane and total hydrocarbon concentrations in Alberta, Canada using machine learning
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
Fugitive emission sources are significant contributors to methane emissions, and time series data on reported emissions from such sources remain underutilized. The Alberta Energy Regulator (AER) has been collecting air quality data since 1986, including methane and total hydrocarbons concentration data. However, this data has not been thoroughly analyzed to forecast air quality trends. Our analysis of the data shows that average methane concentrations measured at most Alberta airshed stations exceed the global average, and the data exhibits increasing and decreasing trends depending on the station. We compared the predictive performance of three machine learning methods: Long Short-Term Memory (LSTM) recurrent neural network, Fully-Connected Neural Network (FC-NN), and Autoregressive Integrated Moving Average (ARIMA), using the AER methane concentration data. Our results indicate that the LSTM neural network model outperforms the other two methods. Also, our findings suggest that the AER methane concentration data can be effectively analyzed and utilized to forecast air quality trends in the region.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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