Under‐ice salinity transport in low‐salinity waterbodies
Bibliographic record
Abstract
Abstract In cold and temperate climates, ice typically covers the surface of waterbodies during winter. Many of these systems are also weakly saline where, unlike seawater, the temperature of maximum density, , is higher than its freezing temperature, . This feature of the equation of state results in a stable temperature stratification when surface waters cool below . Conversely, salts excluded from the growing ice can destabilize the underlying water. Previous laboratory and field experiments demonstrated that excluded salts generate localized overturning and downward transport of salt, despite the persistence of a stable temperature gradient. Those experiments were not able to determine the processes responsible for this transport. Here, we use direct numerical simulations to visualize and characterize the plumes generated when ice excludes salt into a stable temperature gradient. We restrict our analysis to times much earlier than the diffusion timescale of temperature over the domain. We define a mass flux parameter ℜ that considers the strength of the reverse‐temperature stratification relative to the rate of salt exclusion. We identify two types of plumes whose characteristics depend mainly on ℜ: double‐diffusive salt‐fingering plumes and convective plumes. The former encourages transport of salt to the bottom without significantly mixing the temperature stratification, while the latter tends to mix the water column. We apply a scaled mass flux parameter to published laboratory and field observations in low‐salinity systems. These limited observations compare favorably with our numerical analysis.
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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".