Flamingos and drought as drivers of nutrients and microbial dynamics in a saline lake
Bibliographic record
Abstract
Waterbird aggregations and droughts affect nutrient and microbial dynamics in wetlands. We analysed the effects of high densities of flamingos on nutrients and microbial dynamics in a saline lake during a wet and a dry hydrological year, and explored the effects of guano on prokaryotic growth. Concentrations of dissolved organic carbon, total phosphorus and total nitrogen in the surface waters were 2-3 fold higher during the drought and were correlated with salinity. Flamingos stimulated prokaryotic heterotrophic production and triggered cascading effects on prokaryotic abundance, viruses and dissolved nitrogen. This stimulus of heterotrophic prokaryotes was associated with soluble phosphorus inputs from guano, and also from sediments. In the experiments, the specific growth rate and the carrying capacity were almost twice as high after guano addition than in the control treatments, and were coupled with soluble phosphorus assimilation. Flamingo guano was also rich in nitrogen. Dissolved N in lake water lagged behind the abundance of flamingos, but the causes of this lag are unclear. This study demonstrates that intense droughts could lead to increases in total nutrients in wetlands; however, microbial activity is likely constrained by the availability of soluble phosphorus, which appears to be more dependent on the abundance of waterbirds.
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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 | 0.000 | 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 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".