Winter Limnology: How do Hydrodynamics and Biogeochemistry Shape Ecosystems Under Ice?
Bibliographic record
Abstract
Abstract The ice‐cover period in lakes is increasingly recognized for its distinct combination of physical and biological phenomena and ecological relevance. Knowledge gaps exist where research areas of hydrodynamics, biogeochemistry and biology intersect. For example, density‐driven circulation under ice coincides with an expansion of the anoxic zone, but abiotic and biotic controls on oxygen depletion have not been disentangled, and while heterotrophic microorganisms and migrating phytoplankton often thrive at the oxycline, the extent to which physical processes induce fluxes of heat and substrates that support under‐ice food webs is uncertain. Similarly, increased irradiance in spring can promote growth of motile phytoplankton or, if radiatively driven convection occurs, more nutritious diatoms, but links between functional trait selection, trophic transfer to zooplankton and fish, and the prevalence of microbial versus classical food webs in seasonally ice‐covered lakes remain unclear. Under‐ice processes cascade into and from the ice‐free season, and are relevant to annual cycling of energy and carbon through aquatic food webs. Understanding the coupling between state transitions and the reorganization of trophic hierarchies is essential for predicting complex ecosystem responses to climate change. In this interdisciplinary review we describe existing knowledge of physical processes in lakes in winter and the parallel developments in under‐ice biogeochemistry and ecology. We then illustrate interactions between these processes, identify extant knowledge gaps and present (novel) methods to address outstanding questions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".