Phytoplankton nutrient status and mean water column irradiance in Lakes Malawi and Superior
Bibliographic record
Abstract
Abstract Phytoplankton growth in Lake Malawi was moderately nitrogen and phosphorus-deficient according to indicators of phytoplankton nutrient status (particulate C:N, C:P, N:P composition ratios, nitrogen and phosphorus debt assays) and occasionally light-deficient during the period of deepest mixing (July and August). Phytoplankton in Lake Superior was light-deficient during most of the year because of the deeply mixed water column. However, during the stratified period when the mean water column irradiance increased, phytoplankton in Lake Superior became severely phosphorus-deficient according to the same nutrient status indicators used in Lake Malawi as well as alkaline phosphatase activity. Specific rates of carbon uptake normalized to particulate carbon, calculated from photosynthesis at optimum light, were on average three times greater in Lake Malawi than in Lake Superior. We calculated that nitrogen and phosphorus inputs from rivers and precipitation supplied < 15% of the demand for these elements due to photosynthesis for both Lake Malawi and Lake Superior and could not explain the observed difference in phytoplankton nutrient status or specific rate of carbon uptake normalized to particulate carbon. The ratio of nitrogen to phosphorus supplied in Lake Malawi is lower and closer to Redfield ratios than that in Lake Superior. We speculate that the more balanced supply ratio of these nutrients, combined with higher rates of internal regeneration in the warmer deeper mixed layer of Lake Malawi, result in phytoplankton that is less nutrient-deficient and has higher specific rates of carbon uptake normalized to particulate carbon than Lake Superior.
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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 | 0.000 | 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".