Vertical Mixing, Light Penetration and Phosphorus Cycling Regulate Seasonal Algae Blooms in an Ice‐Covered Dimictic Lake
Bibliographic record
Abstract
Abstract Many temperate lakes accumulate sediment derived orthophosphate (PO4) in their hypolimnion during late‐summer deep‐water hypoxia. In dimictic lakes, fall turnover will mix the PO4 through the water column. However, the fate and transport of this primary production limiting nutrient, during winter, is unknown. Does it remain available for the spring bloom, and why does it not trigger a fall bloom in many dimictic lakes? We conducted field observations and supplemented these with three‐dimensional physical biogeochemical numerical simulations to gain a deeper understanding of PO4 transport and cycling within a small dimictic lake from 2011 to 2020. Our focus was particularly on the often‐ice‐covered winter season. We found, the sediment derived PO4 to be only a small portion (∼1%) of the total PO4 load, with most of the load from mineralization (49% ice free, 29% ice covered) and tributary inflows (22%). The accumulated hypolimnetic PO4 increased the water column concentration during fall turnover, but a fall bloom was not initiated, because the associated mixing transported phytoplankton beneath the photic zone. This PO4 remained available in the water column during winter and was combined with under‐ice mineralized PO4 to initiate the spring bloom, in a thin stable layer beneath the ice, as solar radiation increased seasonally during spring.
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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 | 0.001 | 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".