Applying a most probable number method for enumerating planktonic, dissimilatory, ammonium-producing, nitrate-reducing bacteria in oil field waters
Bibliographic record
Abstract
A most probable number (MPN) method was used to enumerate dissimilatory ammonium-producing, nitrate-reducing bacteria (DAP-NRB) in oil field waters and to determine whether they were stimulated by nitrate addition used to control hydrogen sulfide production. An ammonium production medium with 5 carbon and energy sources (acetate, glucose, glycerol, pyruvate, and succinate) and nitrate was used in a 3-tube MPN procedure to enumerate DAP-NRB. These bacteria were detected in 12 of 18 oil field water samples, but they were seldom detected in wellhead samples. Three oil field water samples were amended with nitrate in serum bottles and the numbers of different NRB were determined over a 38-day incubation time. This amendment stimulated increases in the numbers of heterotrophic NRB and autotrophic nitrate-reducing, sulfide-oxidizing bacteria, but DAP-NRB remained a minor portion of these communities. Overall, DAP-NRB were present in many of the oil field waters that were examined but their numbers were low. It appears that DAP-NRB would play a minor role in the consumption of nitrate injected into oil field waters for the control of hydrogen sulfide production.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".