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Record W2499690294 · doi:10.1093/gji/ggw281

A new approach to assess isostatic compensation of topography in continental domain from GOCE gravity gradients

2016· article· en· W2499690294 on OpenAlexaffabout
C. Cadio, Anita Thea Saraswati, Rodolphe Cattin, S. Mazzotti

Bibliographic record

VenueGeophysical Journal International · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Victoria
FundersAgence Nationale de la Recherche
KeywordsIsostasyGeologyGeodesyGravity anomalyPost-glacial reboundGravimetryLithosphereAnomaly (physics)Carbonate compensation depthGeophysicsSeismologyTectonicsGeomorphologyIce sheetPaleontology

Abstract

fetched live from OpenAlex

Estimating how topography is maintained provides insights into the different factors responsible for surface deformations and their relative roles. Here, we develop a new and simple approach to assess the degree of isostatic compensation of continental topography at regional scale from GOCE gravity gradients. We calculate the ratio between the radial gradient observed by GOCE and that calculated from topography only. From analytical and statistical formulations, simple relationships between this ratio and the degree of compensation are obtained under the Airy–Heiskanen isostasy hypothesis. Then, a value of degree of compensation at each point of study area can be easily deduced. We apply our method to the Alaska-Canada Cordillera and validate our results by comparison with a standard isostatic gravity anomaly model and additional geophysical information for this area. Both our GOCE-based results and the isostatic anomaly show that Airy–Heiskanen isostasy prevails for the Yukon Plateau whereas additional mechanisms are required to support topography below the Northwest Territories Craton and the Yakutat collision zone.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.032
GPT teacher head0.249
Teacher spread0.217 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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