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Hydrogeology correction in the gravitational field from satellite data

2018· article· en· W2794277432 on OpenAlexaboutno aff
V.D. Jushkin, Leonid Zotov

Bibliographic record

VenueGeodesy and Cartography · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsGravimeterGravitational fieldGravitational accelerationGeodesyAccelerationGeologySatelliteHydrogeologyField (mathematics)GravitationGeophysicsPhysicsClassical mechanicsGeotechnical engineeringAstronomy

Abstract

fetched live from OpenAlex

The results of repeated measurements of the acceleration of gravity by the Russian absolute ballistic field gravimeter GABL-M on points of oil and gas deposits in the permafrost over a five year period are presented. The changes of gravity acceleration by the absolute gravimeter and GRACE satellite were compared. The results of comparisons of differences gravity acceleration by ballistic gravimeter GABL-M and relative Canadian gravimeters CG5 were [i]shown. The errors in determination of parameters of the gravitational field ballistic gravimeter GABL-M and CG5 gravimeters group were presented. The method of measurement with the gravimeter GABL-M and the method of determining the vertical gradients relative CG5 gravity meters was described. The necessity of introducing corrections of hydrogeology is caused by influence of hydro geological factors on the gravitational field in the permafrost. They are comparable with the values of the field change in the result of gas pumping.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.235
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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