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Record W3152824003 · doi:10.5194/egusphere-egu21-9011

Glacial Isostatic Adjustment Modelling of the Coast Mountains of British Columbia and Southeastern Alaska

2021· article· en· W3152824003 on OpenAlexaffabout
Maximilian Lauch, T. S. James, Lucinda J. Leonard, Yan Jiang, J. A. Henton, Connor Brierley‐Green

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsNatural Resources CanadaUniversity of Victoria
Fundersnot available
KeywordsGeologyPost-glacial reboundLithosphereGlacierIce capsSubductionAdvanced Spaceborne Thermal Emission and Reflection RadiometerInterferometric synthetic aperture radarTectonicsVolcanoGlacial periodGeomorphologyPhysical geographySeismologySynthetic aperture radar

Abstract

fetched live from OpenAlex

The Coast Mountains in British Columbia and southeastern Alaska contain around 9040 km2 of glaciers and ice fields at present. While these glaciers have followed an overall trend of mass loss since the Little Ice Age (or LIA around 300 years before present), the past decade has seen a significant increase in melting rate that is likely to continue due to the effects of climate change. The region is home to a complex tectonic setting, having proximity to the Queen Charlotte-Fairweather transform plate boundary in the northern region and the Cascadia subduction zone (CSZ) in the southern region, which has an associated active volcanic arc underlying the glaciated area. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) glacier melt data collected between 2000 and 2019 represent a melt rate that is averaged between periods of relatively low mass loss (2000-2009) and high mass loss (2010-2019). As a preliminary test, this average melt rate was assumed to be constant back to the LIA. A history of gridded ice thicknesses was calculated to create an ice loading model for input to a series of forward modelling calculations to determine the crustal response. Predictions of vertical crustal motion are compared to available Global Navigation Satellite System (GNSS) measurements of uplift rate to constrain Earth rheology. The results using this simplified loading model favour a thin lithosphere (around 20-40 km thick) and asthenospheric viscosities on the order of 1019 Pa s. These values are significantly lower than those of rheological profiles used in extant global GIA models, but are in general agreement with previous GIA modelling of the forearc region of the CSZ. To improve the glacial history model, the Open Global Glacier Model (OGGM), driven by historic climate data and statistically downscaled climate projections, is being employed to create a more accurate loading model and refine our estimates of Earth rheology and regional crustal motion. The best-fitting models will be employed to separate GIA and tectonic components of crustal motion and to generate improved regional sea-level projections.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.191
Teacher spread0.169 · 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 designSimulation or modeling
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
Published2021
Admission routes2
Has abstractyes

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