Validation of a methane oxidation biosystem design methodology using numerical modeling
Bibliographic record
Abstract
Introduction: Methane oxidation biosystems (MOBs) are cost effective engineered systems capable of catalyzing the transformation of CH4 into CO2 biotically, thereby mitigating emissions from landfills. Method: In this study we validate how accurately one can predict the hydraulic behaviour of a MOB using numerical modeling. More precisely how one can identify the length of unrestricted gas migration (LUGM), a critical design criterion for effective methane abatement biosystems. Laboratory experiments were conducted to obtain the material properties for a compost mixed with plastic pellets, and sand. With the water retention curve and air permeability function, we predicted the hydraulic performance of a MOB using Hydrus-2D. We then designed and constructed a MOB and monitored several key parameters for 12 months. The validation of the design methodology was conducted using field measurements, while actual climatic data was used as input in numerical modeling. Results: The air permeability function was an appropriate activation function for determining LUGM. Accordingly, the predicted hydraulic behaviour matched the measured hydraulic behaviour reasonably well, validating the proposed procedure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".