Using Advanced Spectroscopy and Organic Matter Characterization to Evaluate the Impact of Oxidation on Cyanobacteria
Bibliographic record
Abstract
Drinking water treatment plants throughout the world are increasingly facing the presence of toxic cyanobacteria in their source waters. During treatment, the oxidation of cyanobacteria changes cell morphology and can potentially lyse cells, releasing intracellular metabolites. In this study, a combination of techniques was applied to better understand the effect of oxidation with chlorine, ozone, potassium permanganate, and hydrogen peroxide on two cell cultures (Microcystis, Dolichospermum) in Lake Champlain water. The discrepancy observed between flow cytometry cell viability and cell count numbers was more pronounced for hydrogen peroxide and potassium permanganate than ozone and chlorine. Liquid chromatography with organic carbon and nitrogen detection was applied to monitor the changes in dissolved organic matter fractions following oxidation. Increases in the biopolymer fraction after oxidation with chlorine and ozone were attributed to the release of intracellular algal organic matter and/or fragmentation of the cell membrane. A novel technique, Enhanced Darkfield Microscopy with Hyperspectral Imaging, was applied to chlorinated and ozonated samples. Significant changes in the peak maxima and number of peaks were observed for the cell walls post-oxidation, but this effect was muted for the cell-bound material, which remained relatively unaltered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".