In situ detection of starch-hydrolyzing microorganisms in activated sludge
Bibliographic record
Abstract
Polysaccharides constitute a significant part of the organic matter in domestic wastewater and their hydrolysis plays an important role in their transformation and nutrient removal in activated sludge wastewater treatment plants. However, there is no information available about the identity, ecophysiology, and abundance of starch-hydrolyzing organisms (SHOs) in these plants. In this study, fluorescence in situ enzyme staining with BODIPY fluorescein-labeled starch was applied and optimized to label SHOs expressing alpha-amylase in activated sludge plants. Fluorescence on the surface of bacteria expressing alpha-amylase activity was clearly visualized. In 11 full-scale nutrient-removing wastewater treatment plants examined, the morphotypes of the dominant SHOs were always cocci in clusters of tetrads, short rods in clusters, and some filamentous organisms. The SHOs were identified by combining in situ enzyme staining and FISH using a range of available oligonucleotide probes. All the SHOs observed were Actinobacteria, and most had the phenotype of polyphosphate-accumulating organisms closely related to the genus Tetrasphaera in the family Intrasporangiaceae. The SHOs were present in most of the wastewater treatment plants examined and comprised, in total, up to 11% of bacterial biovolume and thus formed an important part of the microbial communities.
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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) | 0.000 | 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, 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".