Measurement of growth rate of ammonia oxidizing bacteria in partially submerged rotating biological contactor by fluorescent in situ hybridization (FISH)
Bibliographic record
Abstract
Optimization of the nitrification processes in biofilms is important for effective nitrogen removal because nitrification in an aerobic biofilm is considered to be a less than reliable process. Thus, one of the main factors to improve biological nitrogen removal processes is a better understanding of the microbiology and population dynamics of ammonia oxidizing bacteria (AOB) in wastewater treatment biofilms. Although the AOB in wastewater treatment have been qualitatively and quantitatively studied, information on their actual populations and activities is still limited. Therefore, the areal cell density of AOB in domestic wastewater biofilms on a partially submerged rotating biological contactor (RBC) was determined by fluorescent in situ hybridization (FISH) with a set of 16S rRNA-targeted oligonucleotide probes. The growth kinetics of the in situ AOB was also studied. Although low numbers of AOB were found at the deeper layers where oxygen was depleted, they were primarily detected in the upper and middle layers of the biofilm. The maximum specific growth rate (µb,max) and half saturation constant (Ks) of AOB in the biofilm were 0.32 d–1 and 1.7 mM/L of NH4+, respectively. Key words: ammonia oxidizing bacteria (AOB), fluorescent in situ hybridization (FISH), growth rate, rotating biological contactor (RBC).
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".