Environmental factors associated with a toxic bloom of<i>Microcystis aeruginosa</i>
Bibliographic record
Abstract
Environmental factors associated with the occurrence of toxic cyanobacterial blooms and toxin production were investigated during the summers of 1994 and 1995 in Steilacoom Lake, Washington. A pronounced and prolonged toxic bloom of Microcystis aeruginosa occurred during summer 1994 but not during 1995. Lake characteristics that were associated with the toxic bloom in 1994 were higher total phosphorus, decreased water transparency, high water column stability, high surface water temperature and pH, and decreased lake flushing. Decreased water transparency during 1994 may have been due to significantly lower zooplankton abundance. We hypothesize that this decreased transparency was caused by increased planktivory by higher numbers of coho salmon (Oncorhynchus kisutch) fingerlings during 1994 and (or) inhibition of zooplankton grazing by Microcystis. The success of Microcystis over other cyanobacteria was associated with low nitrogen to phosphorus ratios and low nitrate-nitrogen with sufficient ammonium-nitrogen concentrations. Toxin production (i.e., micrograms of microcystin per gram of plankton biomass) was not constant over the duration of detectable toxicity; hence, no relationship was found between Microcystis abundance and microcystin concentration. However, microcystin concentration was positively correlated with increasing soluble reactive phosphorus concentrations between 1 and 10 µg·L -1 , indicating that toxin production may have been limited by phosphorus.
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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 | 0.001 | 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".