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Record W4360994469 · doi:10.1093/pnasnexus/pgad103

The potential for stabilizing Amundsen Sea glaciers via underwater curtains

2023· article· en· W4360994469 on OpenAlexaff
Michael Wolovick, John C. Moore, Bowie Keefer

Bibliographic record

VenuePNAS Nexus · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlacierUnderwaterGeologyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.242
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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