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Record W4252256398 · doi:10.2205/2019iugg-ru-iag

National Report for the IAG of the IUGG 2015–2018

2019· paratext· en· W4252256398 on OpenAlexaboutno aff
Mikhail Gerasimenko, V. L. Gorshkov, В. И. Кафтан, Nikolay S. Kosarev, Zinovy Malkin, B.T. Mazurov, С. Л. Пасынок, G. Pobedinsky, V. V. Popadiev, V. P. Savinykh, R. Sermiagin, Н. В. Шестаков, Г. М. Стеблов, L.S. Sugaipova, А. В. Устинов

Bibliographic record

VenueGeoinformatics research papers · 2019
Typeparatext
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsGeodetic datumGeodesyPolar motionReference frameNorth American Datum of 1927GeopotentialEarth's rotationGeologyGeographyPolitical scienceFrame (networking)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

In this National Report are given major results of researches conducted by Russian geodesists in 2015-2018 on the topics of the International Association of Geodesy (IAG) of the International Union of Geodesy and Geophysics (IUGG).This report is prepared by the Section of Geodesy of the National Geophysical Committee of Russia.In the report prepared for the XXVII General Assembly of IUGG (Canada, Montreal, 8-18 July 2019), the results of principal researches in geodesy, geodynamics, gravimetry, in the studies of geodetic reference frame creation and development, Earth's shape and gravity field, Earth's rotation, geodetic theory, its application and some other directions are briefly described.For some objective reasons not all results obtained by Russian scientists on the problems of geodesy are included in the report.The following institutes participated in the preparation of the Report:

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0650.056

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 head0.090
GPT teacher head0.347
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations13
Published2019
Admission routes1
Has abstractyes

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