Microbial Changes in the Fluorescence Character of Natural Organic Matter from a Wastewater Source
Bibliographic record
Abstract
Natural Organic Matter (NOM) is a mixture of aromatic and aliphatic organic compounds of natural origin in any type of aquatic system. Human activities impact the constituents of NOM, from its production to its fate, particularly in the treatment of domestic waste waters. In this work, the impact of microorganisms isolated from a Waste Water Treatment Plant (WWTP) was investigated to determine the fate of NOM fractions in raw sewage, using fluorescence spectroscopy. Wastewater samples were taken at three different times from a WWTP, and incubated for 4 days under two treatments: 1) “raw sewage”, and 2) “spiked”, i.e., the same raw sewage, spiked with bacteria previously isolated from the WWTP. The incubated waters were analyzed by fluorescence spectroscopy, digitally resolved into NOM components: humic- and fulvic-like, and two types of protein-like, i.e., tryptophan- and tyrosine-like, using a Parallel Factor Analysis routine (PARAFAC). The results demonstrate that the “spiked” samples showed the largest changes with incubation time. The signals of the tryptophan- and tyrosine-like components decreased, suggesting a net microbial digestion of proteinaceous material. In contrast, the fulvic-like signals, and to some extent, the humic-like signals increased, suggesting the production of the associated molecular materials during the incubation period. This study provides direct evidence of human impact on the make-up of NOM: the cultures of microbes found at a WWTP consume the proteinaceous material, whereas humic-like and fulvic-like materials are produced.
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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".