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Record W6974648664 · doi:10.5880/isg.2021.005

The South American gravimetric quasi-geoid: QGEOID2021

2021· dataset· en· W6974648664 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGeoidGravimetric analysisTerrainDigital elevation modelLatitudeGeopotentialGeopotential heightEphemeris

Abstract

fetched live from OpenAlex

The South American gravimetric quasi-geoid model, named QGEOID2021, was computed thanks to a collaboration of several institutions, companies and universities of South America. The model covers the area between 15°N and 60°S in latitude and 100°W and 30°W in longitude, with a grid resolution of 5' x 5'. It is based on 959,404 terrestrial gravimetric points, the XGM2019 global geopotential model up to degree and order 200 and the SRTMv3 digital terrain model. The short wavelengths of the solution were estimated via Fast Fourier Transform (FFT) with the modified Stokes kernel proposed by Vaníček and Kleusberg (1987). On the other hand, long and medium wavelengths were removed and replaced in the framework of a remove-compute-restore procedure. Regions without gravimetric observations were completed using XGM2019 to its full degree. The calculation of the geoid model was performed by the Canadian package SHGEO (Stokes-Helmert Geoid Software). The quasi-geoid model was obtained by the classical geoid/quasi-geoid separation term (Heiskanen and Moritz, 1967). The comparison between height anomalies and GPS/levelling data at 1108 points in Brazil shows differences with an RMS of 41 cm. The geoid model is provided in ISG format 2.0 (ISG Format Specifications), while the file in its original data format is available at the model ISG webpage.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0030.000
Open science0.0060.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.143

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 head0.041
GPT teacher head0.338
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations3
Published2021
Admission routes1
Has abstractyes

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