Phosphorus enrichment and carbon depletion contribute to high <i>Microcystis</i> biomass and microcystin concentrations in Ugandan lakes
Bibliographic record
Abstract
We investigated the factors influencing cyanobacterial biomass and microcystin (MC) concentrations in several Ugandan lakes from September 2008 to February 2009. We characterized thermal structure, light availability, nutrient concentrations, chlorophyll a, phytoplankton δ13C (as an indicator of CO2 limitation), and phytoplankton community composition and abundance as well as MC concentrations. We used these data to test several hypotheses based on previous research in temperate lakes regarding the factors that encourage high cyanobacterial biomass and MC concentrations. Site characteristics that appeared to favor high cyanobacterial biomass (especially Microcystis) included: high total phosphorus concentrations, low total nitrogen to total phosphorus (TN : TP) ratios, and possibly low CO2 availability. Light availability, total nitrogen concentrations, and thermal structure of the water column were not related to cyanobacterial biomass. MC concentrations were strongly related to Microcystis biomass (and were not related to the biomass of any other cyanobacterial taxa), which was positively correlated with total phosphorus and chlorophyll a concentrations. MC cell content may be moderated by CO2 availability, with MC cell quotas tending to be lower where the potential for C‐limitation of photosynthesis was higher. In these phosphorus‐rich tropical lakes, the shallowest study sites were most conducive to the development of large standing crops of Microcystis and high MC concentrations. The environmental conditions that appear to favor high cyanobacterial biomass and MC concentrations in our Ugandan study lakes are similar to what has been observed for temperate 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".