Sediment biomarker, bacterial community characterization of high arsenic aquifers in Jianghan Plain, China
Bibliographic record
Abstract
Representative biomarkers (e.g., n-alkanes), diversity and microbial community in the aquifers contaminated by high concentration of arsenic (As) in different sediment depth (0-30 m) in Jianghan Plain, Hubei, China, were analyzed to investigate the potential mechanism of As enrichment in groundwater. The concentration of As was abundant in top soil and sand, but not in clay. The analysis of the distribution of n-alkanes, CPI values, and wax to total n-alkane ratio (Wax(n)%) indicated that the organic matter (OM) from fresh terrestrial plants were abundant in the shallow sediment. However, n-alkanes have suffered from significant biodegradation from the depth of 16 m to 30 m. The deposition of fresh terrestrial derived organic matters may facilitate the release of As from sediment to groundwater in the sediment of 0-16 m. However, the petroleum derived organic matters may do the favor to the release of As in the deeper section of borehole (16 m to 30 m). The 16S rRNA gene sequences identification indicated that Acidobacteria, Actinomycetes and Hydrogenophaga are abundant in the sediments with high arsenic. Therefore, microbes and organic matters from different sources may play important roles in arsenic mobilization in the aquifers of the study area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".