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Record W4411356239 · doi:10.5194/essd-2025-223

Monitoring the Earth's deformation with the SPOTGINS series

2025· preprint· en· W4411356239 on OpenAlexfundno aff
Alvaro Santamaría‐Gómez, Jean‐Paul Boy, Florent Fériol, Médéric Gravelle, Sylvain Loyer, Samuel Nahmani, Joëlle Nicolas, J.L.G. Pallero, Aurélie Panetier, Arnaud Pollet, Pierre Sakic

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
FundersJapan Aerospace Exploration AgencyUniversity of California, San DiegoNational Oceanic and Atmospheric AdministrationKartverketVlaamse regeringInstitut de Physique du Globe de ParisWuhan UniversityRegione CampaniaNatural Environment Research CouncilIstituto Nazionale di Geofisica e VulcanologiaCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheUniversidad Politécnica de MadridNatural Resources CanadaKorea Astronomy and Space Science InstituteEuropean Space AgencyUniversidade da Beira InteriorCentre National d’Etudes SpatialesUniversity of Hawai'i
KeywordsSeries (stratigraphy)Earth (classical element)Deformation (meteorology)GeologyGeodesyMathematicsOceanographyPaleontologyMathematical physics

Abstract

fetched live from OpenAlex

Abstract. A distributed Global Navigation Satellite System analysis center, designated SPOTGINS, has been established by several research groups that utilize the GINS software and the CNES-CLS precise products. Despite the heterogeneity in their research objectives, the SPOTGINS members apply the same configuration and metadata. The computed global ambiguity-fixed precise point positioning time series are fully consistent among the members, and are subsequently published as a single product. This product facilitates a range of research activities, including but not limited to the precise monitoring of the Earth’s deformation and the water vapor content of the troposphere. A comparison of the SPOTGINS series with published series from the Nevada Geodetic Laboratory solution shows no significant difference in quality.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.753

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 head0.022
GPT teacher head0.208
Teacher spread0.186 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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