Salinity controls phytoplankton response to nutrient enrichment in the Great Salt Lake, Utah, USA
Bibliographic record
Abstract
To examine how salinity and nutrient supply interact to control phytoplankton community composition, nutrient limitation, and dinitrogen (N 2 ) fixation rates in the Great Salt Lake (Utah, USA), we conducted a series of bioassay experiments with plankton from both Gilbert Bay, where salinities are near 160 g·L –1 , and Farmington Bay, where salinities range from 10 to 90 g·L –1 . Six-day nutrient addition bioassay experiments showed that the extant phyto plankton communities in both bays were limited by nitrogen (N). However, in 28- to 30-day factorial bioassay experiments in which both salinities and nutrient supply were manipulated, phosphorus stimulated chlorophyll a as much as 500% when salinities were less than 70 g·L –1 and N 2 -fixing cyanobacteria were present. At salinities greater than 70 g·L –1 , or with additions of combined N, N 2 fixation ceased. When N 2 -fixing cyanobacteria were absent, the plankton community was routinely N-limited regardless of salinity. The results of these experiments suggest that nutrient limitation of phyto plankton communities may change depending on salinity levels, because salinity controls whether N 2 -fixing cyanobacteria will be present in the phytoplankton community. Therefore, both salinity and nutrient supply must be considered when making water quality decisions for hypersaline systems such as the Great Salt Lake.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".