Modeling δD-δ18O Steady-State of Well-Sealed Perennially Ice-Covered Lakes and Their Recharge Source: Examples From Lake Untersee and Lake Vostok, Antarctica
Bibliographic record
Abstract
Perennially ice-covered that are tightly sealed from the atmosphere represent a unique group of polar lakes. In these lakes, the δD-δ18O evolution of the water column and its steady-state is controlled by rates of recharge and freezing at the bottom of the ice cover. We developed a recursive model (FREEZCH9) that takes into account the changing salinity in the water column as a result of freezing and mixes the recharge water to the residual water in well-sealed perennially ice-covered lakes. Our model is tested against datasets from Lake Vostok and is used to assess the dD-d18O mass balance of Lake Untersee and evaluate if the lake is in isotopic steady-state. Our FREEZCH9 simulations fit well the predicted dD-d18O values of Lake Vostok and the overlying accreted ice. Simulations with FREEZCH9 also suggests that Lake Untersee is in isotopic steady-state and that its two input sources (i.e. subaqueous terminus melting of the Anuchin glacier and subglacial contributions) have similar dD-d18O composition. Our modeling demonstrates that Lake Untersee most likely did into receive additional input from surface streams during the last 300-500 years. FREEZCH9 may be also used to determine if the hypothetical groundwater systems of the McMurdo Dry Valleys are fully or partially recharged by subglacial lakes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".