Machine learning for accurate methane concentration predictions: short-term training, long-term results
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
Abstract Although methane emissions from Alberta’s oil and gas sector have decreased in recent years, monitoring these emissions using Continuous Emission Monitoring Systems (CEMS) can be costly. Predictive Emissions Monitoring Systems (PEMS), powered by machine learning, offer an alternative to or can supplement CEMS. However, effective machine learning models for methane emissions prediction rely heavily on the amount of training data. To address this, we compare the prediction performance of different neural network models, including Long Short-Term Memory (LSTM), Stacked LSTM, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), using varying time intervals for training of methane concentration data from Alberta airshed stations. The results showed that the GRU model performed better with shorter datasets, whereas the LSTM and Stacked LSTM models outperformed the GRU and BiLSTM models when trained with more historical data. However, the study found that more training data did not necessarily result in significantly better prediction models.
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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it