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Record W2112898478 · doi:10.5539/apr.v2n2p25

Evaluation of Accuracy of the Geodetic Reference Systems for the Modelling of Normal Gravity Fields of Nigeria

2010· article· en· W2112898478 on OpenAlexvenueno aff
A. A. Okiwelu, Emmanual Emeka Okwueze, Christian Elendu Onwukwe

Bibliographic record

VenueApplied Physics Research · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsGeodetic datumGeodesyReference modelGravitational fieldStandard deviationGravity of EarthReference frameReference dataVertical deflectionGeologyComputer scienceStatisticsMathematicsPhysics

Abstract

fetched live from OpenAlex

The normal gravity fields of Nigeria have been modeled exploiting the Geodetic Reference System 1930 (GRS-30), Geodetic reference system 1967 (GRS-1967) and the world geodetic reference system 1984 (WGS-84). The determination of the normal gravity fields from the three Geodetic Reference Systems were carried out to evaluate the accuracy of one reference datum with respect to another. Descriptive statistics of the normal gravity field values modeled on regional and local scales showed a large difference ( 16.11mGal) between the 1930 reference earth model (GRS-30) and 1967 reference earth model (GRS-67). A large difference was also established between the 1930 and 1984 reference earth models. The large difference in the mean of normal gravity field is attributed to error inherent in the Potsdam value ( 16mGal). However, the small difference in normal gravity field values between the 1967 and 1984 reference systems is a pointer that the choice of either application in geophysical exploration or geodetic applications is of minor importance. The trends in discrepancies between the 1930 and 1967 reference earth models and 1930 and 1984 reference earth models are reflected in the standard deviation and standard error (S.E) of the normal gravity field values.

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)0.004
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.249
GPT teacher head0.356
Teacher spread0.107 · 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 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
Published2010
Admission routes1
Has abstractyes

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