Sulfate-reducing bacteria in leachate-polluted aquifers along the shore of the East China Sea
Bibliographic record
Abstract
The diversity of sulfate-reducing bacteria (SRB) in the aquifer underlying the Laogang Landfill along the shore of the East China Sea was investigated. The DNA extracted from 15 groundwater samples was subjected to PCR amplification of the dissimilatory sulfite reductase (dsr) gene. Full-length dsrAB amplicons (approximately 1.9 kb) were then used to construct 4 clone libraries, while the dsrB amplicons (approximately 350 bp) were used for denaturing gradient gel electrophoresis (DGGE) analysis. The clones in the 4 libraries covered all cultured SRB lineages, as well as a deeply branching clade not affiliated with any cultured SRB. In addition, nearly 80% of the 388 clones in the 4 libraries were similar to sequences of the Deltaproteobacteria, Desulfobacteriaceae, Desulfovibrionales, Syntrophaceae, and Desulfobulbaceae. Furthermore, a wide variety of marine SRB was detected, which indicated that seawater has infiltrated the aquifer. Indeed, the DGGE profiles revealed obvious variations in SRB diversity among the 15 samples, which clustered in accordance with the sulfate concentration of the samples ([SO4(2-)]). Moreover, the sulfate concentrations and SRB diversity along the leachate plume did not show regular variation, which suggests the impact of both groundwater flow and seawater intrusion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".