Commercialization Potential of Industry-Specific Methane Bio-Filters (MBFs)
Bibliographic record
Abstract
Methane Bio-Filters (MBFs) use methanotrophic bacteria to convert methane (CH4), a key greenhouse gas, to less polluting end products. MBFs are designed to treat point-source low volume CH4 emissions as an alternative to flaring and venting activities. This study evaluates six active MBF pilot installations in the oil & gas, agriculture, and waste industries in Western Canada to explore its commercialization potential. This work draws conclusions from research on MBF technical, process and market evaluations. As per the methodology used, economics aspect is the knowledge gap identified. Hence, my research question is whether this technology is economically feasible based on financial analysis of pilot installations? Research concluded economic viability at $10-$16/tCO2e for evaluated designs. The MBF adoption by industries are dependent on the costs and benefits of this technology. This work is multi-disciplinary focusing on energy, environment and economic dimensions. MBFs shows promise, as it grows to shed its weakness with each pilot project.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".