Microbial nutrient limitation in prairie saline lakes with high sulfate concentration
Bibliographic record
Abstract
Most of the lakes on the Canadian prairies are saline (~3 g liter- ’ salt). Sulfate ions are relatively more abundant in these lakes than anywhere else in the world. Studies indicate that some of these lakes do not conform to empirical models which link chlorophyll a to spring total phosphorus concentration. A suite of tests, including nutrient enrichment bioassays, sestonic and protein to carbohydrate ratios, alkaline phos-phatase activity, and 32P-turnover times were used to test microbial nutrient limitation in three prairie saline lakes. Although the concentration of soluble reactive P (SRP) was high (9-3 1 pg liter- I) in two of the lakes, little was available for microbial growth. Bacteria were responsible for 84 and 53 % of the 32P uptake in these two lakes. Production of high levels of alkaline phosphatase by the phytoplankton in one lake appears to keep their intracellular stores of P replete and PN: PP ratios in the P-sufficient range. Striking differences were noted when our data frcm saline lakes were compared to data from freshwater lakes. Our saline lakes were P-deficient at SRP concentrations ~3 1 pg liter-‘, while freshwater lakes were P-deficient at SRP concentrations < 1 pg liter-l High concentrations of dissolved organic C, pH, and ionic composition in saline lakes appear to play a.-ole in the availability of P. According to Hammer (1990), mo St of the lakes on the
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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 | 0.001 | 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".