Planktonic nitrate-reducing bacteria and sulfate-reducing bacteria in some western Canadian oil field waters
Bibliographic record
Abstract
Oil fields that use water flooding to enhance oil recovery may become sour because of the production of H(2)S from the reduction of sulfate by sulfate-reducing bacteria (SRB). The addition of nitrate to produced waters can stimulate the activities of nitrate-reducing bacteria (NRB) and control sulfide production. Many previous studies have focused on chemolithotrophic bacteria that can use thiosulfate or sulfide as energy sources while reducing nitrate. Little attention has been given to heterotrophic NRB in oil field waters. Three different media were used in this study to enumerate various types of planktonic NRB present in waters from five oil fields in western Canada. The numbers of planktonic SRB and bacteria capable of growth under aerobic conditions were also determined. In general, microbial numbers in the produced waters were very low (<10 ml x (-1)) in samples taken near or at wellheads. However, the numbers increased in the aboveground facilities. No thiosulfate-oxidizing NRB were detected in the oil field waters, but other types of NRB were detected in 16 of 18 produced water samples. The numbers of heterotrophic NRB were equal to or greater than the number of sulfide-oxidizing, chemolithotrophic NRB in 12 of 15 samples. These results showed that each of the oil fields contained NRB, which might be stimulated by nitrate amendment to control H(2)S production by SRB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".