The “nutrient pump:” Iron‐poor sediments fuel low nitrogen‐to‐phosphorus ratios and cyanobacterial blooms in polymictic lakes
Bibliographic record
Abstract
Abstract Several lines of evidence from a eutrophic lake show how polymixis enables phosphorus (P) released from anoxic, iron (Fe)‐poor sediments to lower nitrogen‐to‐phosphorus (N : P) ratios and stimulate cyanobacterial blooms. Detailed sediment analyses revealed extensive formation of Fe sulfides, which suppressed porewater Fe levels and prevented sequestration of P in Fe minerals. Experimental additions of Fe significantly decreased the flux of dissolved P from warm, anoxic sediments, increasing N : P ratios in porewater and overlying water. The net midsummer effect of polymixis and P release from Fe‐poor sediments quickly doubled the total P in the euphotic zone during a period of very low external P loading. This internal “nutrient pump” decreased N : P in surface waters and led to a cyanobacterial bloom comprised primarily of diazotrophic Anabaena and Aphanizomenon spp. along with nonheterocystous and potentially toxic Microcystis icthyoblabe and Woronichinia naegelianum. Concentrations of the cyanotoxin, microcystin, in this lake were typically elevated during, or shortly after, episodes of internal P loading. Our study demonstrates an important mechanism underlying the increasing cyanobacterial dominance of weakly stratified eutrophic north temperate lakes, and warns of further increases under a warming climate.
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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 | 0.000 |
| 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".