MétaCan
Menu
Back to cohort
Record W2791055944 · doi:10.1002/2017jf004505

Bedrock Fracture Characteristics as a Possible Control on the Distribution of Surge‐Type Glaciers

2018· article· en· W2791055944 on OpenAlexafffundabout
Jeff W. Crompton, Gwenn E. Flowers, Doug Stead

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBedrockGeologySurgeGlacierOutcropGeomorphologyDiscontinuity (linguistics)

Abstract

fetched live from OpenAlex

Glacier surging has been studied extensively and is understood as a dynamic instability at the glacier bed. Yet an explanation for the heterogeneous distribution of surge‐type glaciers at the scale of a mountain range remains elusive. Here we investigate bedrock discontinuity properties in the basins of 16 surge‐type and nonsurge‐type glaciers in the St. Elias Mountains of Yukon, Canada. Using scaled photographs of bedrock outcrops at the margins of each glacier, we digitize traces of the bedrock discontinuities and with automated purpose‐built software, quantify discontinuity properties that are a function of length, orientation, and spacing of bedrock fractures. We obtain an unexpected result: outcrops in the basins of surge‐type glaciers are less fractured than those in the basins of nonsurge‐type glaciers. We hypothesize that the degree of bedrock fracture may control the extent and location of a clast‐rich till transition zone at the glacier bed. This zone would provide flow resistance conducive to the development of an ice reservoir and thus to surging behavior. To reconcile our observations with the global distribution of surge‐type glaciers, we speculate that surge‐type glaciers may occur in geological settings characterized by an intermediate range of bedrock fracture.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.305
Teacher spread0.266 · 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

Citations22
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Geophysical Research Earth SurfaceSame topicCryospheric studies and observationsFrench-language works237,207