Bacterial community changes along a salinity gradient in a Chinese wetland
Bibliographic record
Abstract
We analyzed the composition and diversity of a bacterial community to determine its response to increasing salinity in the Xiangsi Lake wetland in the arid region of northwest China. We studied 12 sites, ranging from freshwater to saltwater habitats, using polymerase chain reaction (PCR)-based terminal restriction fragment length polymorphism (T-RFLP) and sequencing of the 16S rRNA genes of 5 selected samples. Cluster analysis and phylogenetic analysis revealed that the bacterial community changed significantly in response to increased salinity within the small-scale wetland (50.8 km(2)). Detailed analysis showed that (i) Betaproteobacteria can maintain balanced growth over a salinity range (from 0.34 to 6.86 g/L) through intergenus changes in community structure, followed by a sharp decrease in relative abundance (from 62.2% to 16.0%) when salinity reaches 26.18 g/L; (ii) salt-sensitive and halophobic taxa were progressively replaced by halotolerant and halophilic taxa with increasing salinity; (iii) bacterial diversity was lowest at intermediate salinity levels (6.86 g/L); and (iv) an increasing percentage of unclassified bacterial taxa were found with increasing salinity. This study has implications to improve understanding of bacterial community response to water salinization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".