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Record W1961316012 · doi:10.1002/2013gl057973

Measuring water accumulation rates using GRACE data in areas experiencing glacial isostatic adjustment: The Nelson River basin

2013· article· en· W1961316012 on OpenAlexafffundabout
A. Lambert, Jianliang Huang, Garth van der Kamp, J. A. Henton, S. Mazzotti, T. S. James, N. Courtier, Alan Barr

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of VictoriaEnvironment and Climate Change CanadaGeological Survey of CanadaNatural Resources Canada
FundersNatural Resources CanadaNational Oceanic and Atmospheric AdministrationDeutsches Zentrum für Luft- und RaumfahrtManitoba Hydro
KeywordsPost-glacial reboundGeologyDrainage basinAnomaly (physics)GeodesyStructural basinGlacial periodGlobal Positioning SystemGeomorphologyWater levelHydrology (agriculture)Geotechnical engineeringCartographyGeography

Abstract

fetched live from OpenAlex

Abstract Gravity Recovery and Climate Experiment (GRACE) satellite‐derived total water storage can be obscured by glacial isostatic adjustment. In order to solve this problem for the Nelson River drainage basin in Canada, a gravity rate map from 110 months (June 2002 to October 2011) of GRACE gravity data was corrected for glacial isostatic adjustment using an independent gravity rate map derived from updated GPS vertical velocities. The GPS‐based map was converted to equivalent gravity rate using a transfer function developed from GPS and absolute‐g data at colocated sites. The corrected GRACE gravity rate map revealed a major positive anomaly within the drainage basin, which was independently shown by hydrological data to be due to changes in water storage. The anomaly represents a cumulative increase at its center of about 340 mm of water, reflecting a progression from extreme drought to extremely wet conditions.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.138
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.209
GPT teacher head0.344
Teacher spread0.135 · 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.

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

Citations40
Published2013
Admission routes3
Has abstractyes

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