MétaCan
Menu
Back to cohort
Record W4392663225 · doi:10.5194/egusphere-egu24-22538

Climate trends derived from long-term ground-based GNSS-derived Zenith Total Delay (ZTD)

2024· preprint· en· W4392663225 on OpenAlexaffabout
Marcelo C. Santos, Kyriakos Balidakis, Anna Kloss, R. Pacione, Jordan Rees

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsZenithGNSS applicationsTerm (time)GeodesyEnvironmental scienceMeteorologyGlobal Positioning SystemGeographyPhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We present findings from an ongoing investigation into the evaluation of long-term trends in ground-based GNSS-derived Zenith Total Delay (ZTD) for potential integration into climate models, either for assimilation or validation purposes. Our analysis focuses on ZTD time series obtained from six REPRO3 IGS Analysis Centers (ACs) – COD, ESA, GFZ, GRG, JPL, and TUG – spanning 20 years or more. Thirty stations from the IGS global network were selected for this study. The ZTD time series underwent a homogenization process, utilizing ERA-5 derived ZTDs as a reference, followed by daily value averaging to minimize potential discrepancies arising from diverse estimation strategies employed by individual ACs. Similar averaging procedures were applied to ERA-5 ZTDs and the IGS tropo-product if already reprocessed in REPRO3. Two combinations, employing weighted mean and a robust least median of squares, were generated from the six homogenized ACs, serving as quality control measures for each AC. Analysis of trends in each of the nine ZTD time series was conducted in both time and frequency domains, revealing geographical variations in results. For instance, at station ALBH in Canada, the inter-AC scatter was 0.47 mm/decade for trends, 0.11 mm for annual amplitudes, and 0.29 degrees for annual phases.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.244
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicGNSS positioning and interferenceFrench-language works237,207