Winter in two phases: Long‐term study of a shallow reservoir in winter
Bibliographic record
Abstract
Abstract Climate‐driven decreases in ice‐cover duration have the potential to impact lake ecosystems, yet we have only partial understanding of the effects of winter conditions on physical, chemical, and biological properties of lakes. We used 39 years of monitoring data to examine under‐ice changes in nutrients, oxygen, and phytoplankton in a shallow drinking‐water reservoir. Two phases of winter were identified. Early winter was characterized by declining oxygen. In this phase, there were increases in specific conductance and concentrations of ammonium ( ‐N) and soluble reactive phosphorus (SRP). In the month prior to ice off these trends reversed themselves and phytoplankton began to increase. Specific conductance declined as meltwater entered the lake. Nutrients (SRP and ‐N) declined, concurrent with increases in chlorophyll a and oxygen during late winter. This work demonstrates that chemical and biotic changes through winter are highly time dependent and differ between early and late winter phases. The late winter phase is often unstudied because of unsafe ice conditions, but here, the “spring” bloom commonly occurs in late winter under ice. The phases of winter, which are likely driven by changes in light, must be considered as we work to understand how diverse lakes will respond to declining periods of ice cover, and what drives differences in the spring ecology of diverse ice‐covered lakes.
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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 | 0.000 | 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".