Examining the influence of substrates and temperature on maximum specific growth rate of denitrifiers
Bibliographic record
Abstract
Facilities across North America are designing plants to meet stringent limits of technology (LOT) treatment for nitrogen removal (3-5 mg/L total effluent nitrogen). The anoxic capacity requirements for meeting LOT treatment are dependent on the growth rates of the denitrifying organisms. The Blue Plains Advanced Wastewater Treatment Plant (AWTP) is one of many facilities in the Chesapeake Bay region that is evaluating its ability to meet LOT treatment capability. The plant uses methanol as an external carbon source in a post-denitrification process. The process is very sensitive to denitrification in the winter. One approach to improve anoxic capacity utilization is to use an alternative substrate for denitrification in the winter to promote the growth of organisms that denitrify at higher rates. The aim of this study was to evaluate denitrification maximum specific growth rates for three substrates, acetate, corn syrup and methanol, at two temperatures (13 degrees C and 19 degrees C). These temperatures approximately reflect the minimum monthly and average annual wastewater temperature at the Blue Plains AWTP. The results suggest that the maximum specific growth rate (mu(max)) for corn syrup (1.3 d(-1)) and acetate (1.2 d(-1)) are higher than that for methanol (0.5d(-1)) at low temperature of 13 degrees C. A similar trend was observed at 19 degrees C.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".