Nitrifier and denitrifier molecular operational taxonomic unit compositions from sites of a freshwater estuary of Chesapeake Bay
Bibliographic record
Abstract
Temporal and spatial changes in the molecular operational taxonomic unit (OTU) compositions of bacteria harboring genes for nitrification and denitrification were assessed using denaturing gradient gel electrophoresis (DGGE), clone-based DNA sequencing of selected PCR products, and analyses of ammonium and organic matter concentrations. Sediment, overlying water, and pore-water samples were taken from different vegetated sites of Jug Bay National Estuarine Research Reserve, Maryland, during spring, summer, and fall 2006. OTU richness and the diversities of nitrifiers and denitrifiers were assessed by the presence of bands on DGGE gels, both ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) were seasonally dependent. AOB OTU richness was highest in the summer when NOB richness was decreased, whereas NOB richness was highest in the spring when AOB richness was decreased. The OTU diversities of nitrifiers did not correlate with ammonium concentrations, organic matter concentrations, or the presence of vegetation. The OTU diversities of denitrifiers possessing either the nirK or nosZ genes were not seasonally dependent but were positively correlated with organic matter content (p = 0.0015, r2 = 0.27; p < 0.0001, r2 = 0.39, respectively). Additionally, the presence of vegetation significantly enhanced nosZ species richness (Wilcoxon/Kruskal-Wallis test, p < 0.008), but this trend was not seen for nirK OTU richness. Banding patterns for nirK OTUs were more similar within sites for each season compared with any of the other genes. Over all seasons, nirK OTU richness was highest and AOB and nosZ OTU richness were lowest (Wilcoxon/Kruskal-Wallis test, p < 0.0001). High levels of sequence divergence among cloned nirK PCR products indicate a broad diversity of nirK homologs in this freshwater estuary.
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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 | 0.001 | 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) | 0.001 | 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".