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Record W4200135035 · doi:10.31237/osf.io/6qth3

Surface velocity and ice thickness of the Müller ice cap, Axel Heiberg Island

2021· preprint· en· W4200135035 on OpenAlexaboutno aff
Ann-Sofie Priergaard Zinck

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyIce divideIce streamAntarctic sea iceIce shelfIce coreArctic ice packGlacier morphologySea iceCryosphereIce sheetDrillingArcticSea ice thicknessDrift iceGlacierGeomorphologyPhysical geographyClimatologyOceanographyGeographyMaterials science

Abstract

fetched live from OpenAlex

Muller ice cap is situated on Axel Heiberg Island in Arctic Canada. It is characterised by a mountanious region separating the ice cap in the east from the outlet glaciers in the west. Research has taken place on the outlet glaciers of the ice cap since 1959, but only limited research has been conducted on the main ice cap, and no full depth ice cores have ever been drilled. The interesting location of the ice cap facing the Arctic Ocean, the chance of finding ice dating back to the Innuitian Ice Sheet, and the fact that no full depth ice cores have been drilled, makes it an obvious place to do so. In order to achieve a long and undisturbed chronology of the ice core, one needs to find a location for the drilling site, where there is a great ice thickness, low surface velocity and little melt. In this project the aim is to make surface velocity maps of the ice cap and estimate the ice thickness to be able to come up with suggestions of possible drill site areas.Surface velocities are calculated using feature tracking of optical satellite images from the Landsat satellites in the period of 2004-2019. A median velocity map of all Landsat 8 velocity maps is used as validation in modelling the ice thickness and in the investigation of possible drill site areas.To estimate the ice thickness various methods are used and are being compared to the ice thickness measured by Operation IceBridge. The first method is an iterative inverse method where the ice sheet model PISM works as a forward model. The model is found to work rather well on the ice cap, with a root mean squared error (RMS) of 138.9 m, but overestimates the ice thickness on the outlet glaciers. The second model uses a simple inversion of the shallow ice approximation. It overestimates the ice thickness in areas with low surface slope, but has a RMS of 131.4 m on the ice cap. The third and and fourth models uses Monte Carlo sampling methods of the shallow ice approximation without and with sliding, respectively. The latter uses an initial ice thickness guess, and the modelled ice thickness was proofed not to differ from that initial guess at all. The RMSs on the icecap of the two models were found to be 132.1 m and 129.9 m, respectively. Finally, the fifth model uses the PISM setup but with an initial geometry defined by the SIA inversion. The RMS on the ice cap is found to be 135.4 m.Based on the median Landsat 8 surface velocity map, the modelled ice thicknesses and the surface elevation from the Arctic Digital Elevation Model, a map of suggested drill site areas is made. The site which fulfilled the criteria the most is located at 526629 m easting and 8866463 m northing in UTM zone 15N. In this site the surface velocity is 1.2 m yr−1, the surface elevation is 1804 m and the modelled ice thicknesses varies from 535-579 m. The melt in this area is estimated to be less than 20 melt days per year based on the backscatter from Sentinel-1.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.911

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.224
Teacher spread0.197 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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