Quantifying functional gene populations: comparing gene abundance and corresponding enzymatic activity using denitrification and nitrogen fixation in pulp and paper mill effluent treatment systems
Bibliographic record
Abstract
The relationship between the abundance of three functional genes and their corresponding biochemical reaction rates was investigated in several activated sludge and mill effluent microbial communities. Gene probes were prepared for two key denitrification genes (nirS and nirK) and for one nitrogen-fixation gene (nifH) and were validated using a variety of strains of known nir and nif genotype. ATP-based measures of viable cell numbers were used to provide total population sizes. In certain microbial communities (activated sludge enrichment cultures and multiple samples taken from the same mill primary clarifier), a strong correlation was observed between gene abundance and biochemical activity rates. However, when comparing several different nonenriched activated sludge bioreactors and separate primary clarifier microbial communities, the ratio of specific gene abundance to biochemical activity rates varied widely. These results suggest that in cases where a microbial community is not fully induced for a given biochemical activity or when very different communities are compared, quantitative gene probing can give a better measure of a community's potential to carry out the encoded function than can the relevant biochemical assay. However, the gene quantitation method employed here probably underestimated the true number of probed genes present in the microbial communities due to nirS and nifH genes in the communities having reduced DNA sequence similarity with the probes used.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".