Internal nutrient loading may increase microcystin concentrations in freshwater lakes by promoting growth of<i>Microcystis</i>populations
Bibliographic record
Abstract
Nutrient release from lake sediments may increase concentrations of harmful algal toxins – such as microcystins – by stimulating blooms of toxigenic cyanobacteria. This hypothesis is supported by a series of experiments in which intact cores of sediment were incubated under different environmental conditions, after which the water overlying the sediments was harvested as a culture medium for growing a toxic strain of the common cyanobacterium Microcystis. Both littoral and profundal sediments from Lake Kinneret, the largest freshwater lake in Israel, released substantial amounts of dissolved phosphorus (1.0 and 4.0 mg.m−2.d−1, respectively) and nitrogen (44.2 and 24.3 mg.m−2.d−1, respectively) under simulated summer conditions in the laboratory. In comparison, nutrient fluxes from sediments under simulated winter conditions were considerably smaller or negative. The addition of nutrient-rich overlying water harvested from profundal sediments, and to a lesser extent from littoral sediments, increased both chlorophyll a and microcystin concentrations in Microcystis cultures. In contrast, when Microcystis cells were inoculated in natural surface waters only, the cultures did not grow or produce microcystins, and soon collapsed. This study provides experimental evidence of a link between internal nutrient loading from sediments and microcystin concentrations in freshwaters, and demonstrates how environmental factors may indirectly exert control over toxin concentrations in freshwater lakes.
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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".