Biogas Recognition over Landfill with MOS Gas Sensors Array and PCA-quantile Regression
Bibliographic record
Abstract
The monitoring of biogas emissions has become a concern regarding the greenhouse effect of methane emitted over the waste treatment plants. Besides, chemical sensors array as metal oxide semiconductor (MOS) one has the technical potential to manage, in real-time, these emissions. The use of these devices could help to organise fast, easy and regular controls regarding their low cost and their low power consumption. The data treatment of MOS sensors array signal is a crucial aspect for their use. In the purpose of performing biogas monitoring over a landfill, a prediction model based on PCA and quantile regression has been developed and tested over a landfill. The results showed that it was possible to recognise areas easily with biogas emissions and those with no emissions. Following these promising results, a mapping of methane concentrations over the landfill could capture possible emissions trends or patterns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".