Modeling brine and nutrient dynamics in Antarctic sea ice: The case of dissolved silica
Bibliographic record
Abstract
Sea ice ecosystems are characterized by microalgae living in brine inclusions. The growth rate of ice algae depends on light and nutrient supply. Here, the interactions between nutrients and brine dynamics under the influence of algae are investigated using a one‐dimensional model. The model includes snow and ice thermodynamics with brine physics and an idealized sea ice biological component, characterized by one nutrient, namely, dissolved silica (DSi). In the model, DSi follows brine motion and is consumed by ice algae. Depending on physical ice characteristics, the brine flow is either advective, diffusive, or turbulent. The vertical profiles of ice salinity and DSi concentration are solutions of advection‐diffusion equations. The model is configured to simulate the typical thermodynamic regimes of first‐year Antarctic pack ice. The simulated vertical profiles of salinity and DSi qualitatively reproduce observations. Analysis of results highlights the role of convection in the lowermost 5–10 cm of ice. Convection mixes saline, nutrient‐poor brine with comparatively fresh, nutrient‐rich seawater. This implies a rejection of salt to the ocean and a flux of DSi to the ice. In the presence of growing algae, the simulated ocean‐to‐ice DSi flux increases by 0–115% compared to an abiotic situation. In turn, primary production and brine convection act in synergy to form a nutrient pump. The other important processes are the flooding of the surface by seawater and the percolation of meltwater. The former refills nutrients near the ice surface in spring. The latter, if present, tends to expell nutrients from the ice in summer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".