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Record W2118662829 · doi:10.4236/ns.2012.47056

Thermal convection in ice sheets: New data, new tests

2012· article· en· W2118662829 on OpenAlexaboutno aff
Terence J. Hughes

Bibliographic record

VenueNatural Science · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersCenter for Remote Sensing of Ice Sheets
KeywordsGeologyConvectionThermalMeteorologyClimatologyGeophysicsPhysics

Abstract

fetched live from OpenAlex

Thermal convection in the Antarctic Ice Sheet was proposed in 1970. Demonstrating its existence proved to be elusive. In 2009, tributaries to ice streams were postulated as the surface expression of underlying thermal convection rolls aligned in directions of advective ice flow. Two definitive tests of this hypothesis are now possible, using highly accurate ice elevations and velocities provided by the European, Japanese, and Canadian Space Agencies that allow icestream tributaries and their velocities to be mapped. These tests are 1) measuring lowering of tributary surfaces to see if lowering is due only to advective ice thinning, or also requires lowering en masse in the broad descending part of convective flow, and 2) measuring transverse surface ice velocities to see if ice entering tributaries from the sides increases while crossing lateral shear zones, as would be required if this flow is augmented by convective flow ascending in the narrow side shear zones and diverted into tributaries by advective ice flow. If 1) and 2) are applied to tributaries converging on Byrd Glacier, the same measurements can be conducted when tributaries pack together to become "flow stripes" down Byrd Glacier and onto the Ross Ice Shelf to see if 2) is reduced when lateral advection stops. This could determine if thermal convection remains active or shuts down as ice thins. Thermal convection in the Antarctic Ice Sheet would raise three questions. Can it cause the ice sheet to self-destruct as convective flow turns on and off? Does it render invalid climate records extracted at depth from ice cores? Can the ice sheet be studied as a miniature mantle analogous in some respects to Earth's mantle?

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.005
Science and technology studies0.0020.014
Scholarly communication0.0070.030
Open science0.0050.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.002

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 head0.045
GPT teacher head0.275
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2012
Admission routes1
Has abstractyes

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