The relationship between the algal toxin microcystin and the chemical, physical and biological parameters in Lake Winnipeg
Bibliographic record
Abstract
Cyanobacterial algae blooms are of rising concern all over the world because of their increasing abundance and toxin release. Using an 18-year dataset (1999 to 2017), correlations between chemical, physical and biological parameters and the microcystin toxin in Lake Winnipeg were investigated. Multi-panel scatterplots with Pearson correlation coefficients found total boron in euphotic dataset and Anabaena biomass in the surface dataset had the strongest positive correlations. N variables such as total N, total Kjeldahl N, nitrate + nitrite and the N:P ratio may aid in controlling microcystin levels in Lake Winnipeg. Higher toxins correlated with higher N variables in the surface dataset (total N and total Kjaldahl N) and euphotic dataset (N:P ratio) and higher toxins and lower N variables correlated in the euphotic dataset (dissolved NO3 + NO2). Total suspended solids had a positive correlation with toxin levels in the surface dataset; Secchi disk depth and wind speed had negative correlations. Overall, I found that algal toxin concentrations in the epilimnion of Lake Winnipeg correlated best with several metrics of nitrogen availability, water clarity, Anabaena biomass and boron concentrations .
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".