Nitric Oxide Reductase-Targeted Real-Time PCR Quantification of Denitrifier Populations in Soil
Bibliographic record
Abstract
The quantification of denitrifying bacteria is a component in the further understanding of denitrification processes in the environment. Real-time PCR primers were designed to target two segments of the denitrifier population (cnorB(P) [Pseudomonas mandelii and closely related strains] and cnorB(B) [Bosea, Bradyrhizobium, and Ensifer spp.]) in agricultural soils based on functional cnorB (nitric oxide reductase) gene sequences. Total population numbers were measured using 16S rRNA gene real-time PCR. Two soil microcosm experiments were conducted. Experiment 1 examined the response of the indigenous soil microbial population to the addition of 500 mg/kg glucose-C daily over 7 days in soil microcosms. Changes in the total population were correlated (r = 0.83) between 16S rRNA gene copy numbers and microbial biomass carbon estimates. Members of the cnorB(P) population of denitrifiers showed typical r-strategy by being able to increase their proportion in the total population from starting levels of <0.1% to around 2.4% after a daily addition of 500 mg/kg glucose-C. The cnorB(B) guild was not able to increase its relative percentage of the total population in response to the addition of glucose-C, instead increasing copy numbers only in proportion with the total population measured by 16S rRNA genes. Experiment 2 measured population dynamics in soil after the addition of various amounts of glucose-C (0 to 500 mg/kg) and incubation under denitrifying conditions. cnorB(P) populations increased proportionally with the amount of glucose-C added (from 0 to 500 mg/kg). In soil microcosms, denitrification rates, respiration, and cnorB(P) population densities increased significantly with increasing rates of glucose addition. cnorB(B) guild densities did not increase significantly under denitrifying conditions in response to increasing C additions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".