The potential for stabilizing Amundsen Sea glaciers via underwater curtains
Bibliographic record
Abstract
Rapid sea level rise due to an ice sheet collapse has the potential to be extremely damaging the coastal communities and infrastructure. Blocking deep warm water with thin flexible buoyant underwater curtains may reduce melting of buttressing ice shelves and thereby slow the rate of sea level rise. Here, we use new multibeam bathymetric datasets, combined with a cost-benefit model, to evaluate potential curtain routes in the Amundsen Sea. We organize potential curtain routes along a "difficulty ladder" representing an implementation pathway that might be followed as technological capabilities improve. The first curtain blocks a single narrow (5 km) submarine choke point that represents the primary warm water inflow route towards western Thwaites Glacier, the most vulnerable part of the most vulnerable glacier in Antarctica. Later curtains cross larger and deeper swaths of seabed, thus increasing their cost, while also protecting more of the ice sheet, increasing their benefit. In our simple cost-benefit analysis, all of the curtain routes achieve their peak value at target blocking depths between 500 and 550 m. The favorable cost-benefit ratios of these curtain routes, along with the trans-generational and societal equity of preserving the ice sheets near their present state, argue for increased research into buoyant curtains as a means of ice sheet preservation, including high-resolution fluid-structural and oceanographic modeling of deep water flow over and through the curtains, and coupled ice-ocean modeling of the dynamic response of the ice sheet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".