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Record W7015683511

Tracking changes in groundwater storage from GNSS geodesy in the Great Lakes region

2022· article· en· W7015683511 on OpenAlexaboutno aff

Bibliographic record

VenueThe Mathematics Enthusiast · 2022
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsGNSS applicationsContext (archaeology)GroundwaterGlobal Positioning SystemDeformation (meteorology)Displacement (psychology)Surface (topology)Snow
DOInot available

Abstract

fetched live from OpenAlex

The Great Lakes (Superior, Huron, Ontario, Michigan, and Erie) make up for one-fifth of the freshwater surface area on the Earth (NOAA, 2021). Displacements of the Earth’s surface are caused by quasi-static loads at or near the surface, including surface water, soil moisture and groundwater, and can be predicted by evaluating a convolution integral over the loaded region. In this study, we aim to infer changes in groundwater storage in the Great Lakes region of the central U.S. at sub-monthly time scales. Global Navigation Satellite System (GNSS) data from the Network of the Americas (NOTA; previously known as PBO) are used to estimate 3-D displacements of Earth’s surface (east, north, up) caused by surface loading. GNSS position series represent the superposition of many different signals, from which the groundwater loading signal must be isolated. We therefore model and remove predicted deformation due to soil moisture loading (NLDAS model), snow loading (SNODAS model), atmospheric-pressure loading (ECMWF model), non-tidal oceanic loading (MPIOM model), background global mass load changes outside of the Great Lakes region (GRACE model), and lake loading (NOAA model) from the GNSS position series. Predictions are computed using a Python-based toolkit called LoadDef to model the elastic deformation of the Earth caused by surface loading. Residual displacements between the observed GNSS measurements and the predicted surface deformation from LoadDef are assessed in the context of unmodeled or mismodeled loading sources, and other uncertainties in the GNSS analysis and loading predictions. The root-mean-square error (RMSE) reduction for vertical displacement ranges between -20 to 40 percent, suggesting that the “known” loading models cannot account for most of the scatter in the GNSS time series and that the residual series can be analyzed for signatures of groundwater deformation. Principal component one (PC1) from a principal component analysis (PCA) of 67 GPS stations accounts for 23.24% of the variability in the vertical residual displacements. We hypothesize that PC1 represents the groundwater fluctuations in the Great Lakes region.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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