<i>Microbiology to Help Solve Our Energy Needs</i>
Bibliographic record
Abstract
Our society depends greatly on fossil fuels, and the environmental consequences of this are well known and include significant increases of the CO(2) concentration in the earth's atmosphere. Although microbiology has traditionally played only a minor role in fossil-fuel extraction, two novel key discoveries indicate that this may change. First, the realization that oil components can be converted to methane and CO(2) by methanogenic consortia in the absence of electron acceptors (oxygen, nitrate, sulfate) explains how much of the world's oil has been biodegraded in situ. In addition to inorganic nutrients, only water is needed for these methanogenic conversions. Hence, continued methanogenic biodegradation may have shaped the heavy-oil reservoirs that are so prevalent today. The potential to exploit these reactions, for example, by in situ gasification, is currently being actively investigated. Second, injection of nitrate in oil and gas fields can lower sulfide concentrations. High sulfide concentrations, caused by the action of sulfate-reducing bacteria (SRB), are associated with increased risk of corrosion, reservoir plugging (through precipitated sulfides), and human safety. Nitrate injection into an oil field stimulates subsurface heterotrophic nitrate-reducing bacteria (hNRB) and nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB). Nitrite, formed by these NRB by partial reduction of nitrate, is a strong and specific SRB inhibitor. Nitrate injection has, therefore, promise in positively controlling the oil-field sulfur cycle. There is now more interest in and potential to apply petroleum microbiology than there has been in the past, allowing microbiologists to contribute to a sustainable energy future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.056 |
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".