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Record W2683344315 · doi:10.1002/2016jf004154

The periodic topography of ice stream beds: Insights from the Fourier spectra of mega‐scale glacial lineations

2017· article· en· W2683344315 on OpenAlexaboutno aff
Matteo Spagnolo, T. C. Bartholomaus, Chris D. Clark, Chris R. Stokes, Nigel Atkinson, Julian A. Dowdeswell, Jeremy C. Ely, Alastair G C Graham, Kelly Hogan, Edward C. King, Robert D Larter, Stephen J. Livingstone, Hamish D. Pritchard

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersBritish Antarctic SurveyRice UniversitySight Research UKStockholms UniversitetNatural Environment Research CouncilUniversity of Idaho
KeywordsGeologyIce streamLineationFluvialGeomorphologyGlacial periodIce sheetBedformAmplitudeLandformGeophysicsPaleontologyCryosphereSea iceClimatologyTectonicsPhysicsStructural basin

Abstract

fetched live from OpenAlex

Abstract Ice stream bed topography contains key evidence for the ways ice streams interact with, and are potentially controlled by, their beds. Here we present the first application of two‐dimensional Fourier analysis to 22 marine and terrestrial topographies from 5 regions in Antarctica and Canada, with and without mega‐scale glacial lineations (MSGLs). We find that the topography of MSGL‐rich ice stream sedimentary beds is characterized by multiple, periodic wavelengths between 300 and 1200 m and amplitudes from decimeters to a few meters. This periodic topography is consistent with the idea that instability is a key element to the formation of MSGL bedforms. Dominant wavelengths vary among locations and, on one paleo ice stream bed, increase along the direction of ice flow by 1.7 ± 0.52% km −1 . We suggest that these changes are likely to reflect pattern evolution via downstream wavelength coarsening, even under potentially steady ice stream geometry and flow conditions. The amplitude of MSGLs is smaller than that of other fluvial and glacial topographies but within the same order of magnitude. However, MSGLs are a striking component of ice stream beds because the topographic amplitude of features not aligned with ice flow is reduced by an order of magnitude relative to those oriented with the flow direction. This study represents the first attempt to automatically derive the spectral signatures of MSGLs. It highlights the plausibility of identifying these landform assemblages using automated techniques and provides a benchmark for numerical models of ice stream flow and subglacial landscape evolution.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.303
Teacher spread0.261 · 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.

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

Citations44
Published2017
Admission routes1
Has abstractyes

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