Deciphering the diversity and concentrations of cyanopeptides from blooms in Ontario and Quebec, Canada
Bibliographic record
Abstract
E. McCann, D. McMullin* Cyanobacteria blooms release mixtures of biologically active compounds into freshwater systems. These poorly studied compounds pose undetermined risks to both human and ecosystem health, and negatively impact local economies that depend on freshwater resources. While microcystins are extensively studied, little is known about the chemistry, structural diversity, toxicology and environmental concentrations of less studied cyanopeptide groups such as, anabaenopeptins, cyanobactins, cyanopeptolins, microginins and aeruginosins. The application of mass spectrometry and metabolomic data processing techniques are powerful tools for deciphering the mixtures of compounds released by cyanobacteria. A Non-targeted high resolution tandem mass spectrometry-based metabolomics approach was used to detect more than one-hundred unique cyanopeptides from fifty-five bloom samples collected from fifteen watercourses near the city of Ottawa in Canada. Microcystins and select other cyanopeptides were quantified with reference materials. The concentrations of other cyanopeptide groups for example, anabaenopeptins cyanopeptolins and microginins, were determined semi-quantitatively. Sixty of the compounds detected contributed significantly to the variation in cyanopeptide profiles between watercourses. The most commonly detected cyanopeptide groups were anabaenopeptins (thirty-three congeners) and cyanopeptolins (thirty-two congeners). Microcystins were detected in forty-one of the fifty-five bloom samples, where microcystin-LR was detected most often; however, microcystin-LA amounts were consistently the highest when detected for this toxin group.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".