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Record W2591636759 · doi:10.1007/978-3-030-13901-8_6

National Status Reports

2019· book-chapter· en· W2591636759 on OpenAlexaffabout
Guergana Guerova, Gregor Möller, Eric Pottiaux, Hugues Brenot, Roeland Van Malderen, Haris Haralambous, Filippos Tymvios, Jan Douša, Michal Kačmařík, Kryštof Eben, Henrik Vedel, Kalev Rannat, Rigel Kivi, Ari‐Matti Harri, Olivier Bock, Jean‐François Mahfouf, Jens Wickert, Galina Dick, Roland Potthast, Susanne Crewell, Christos Pikridas, Nicholas Zinas, Athanassios Ganas, Máté Mile, Sune Thorsteinsson, Benedíkt G. Ófeigsson, Yuval Reuveni, S. O. Krichak, R. Pacione, G. Bianco, Riccardo Biondi, Gintautas Stankūnavičius, Felix Norman Teferle, Jarosław Bosy, Jan Kapłon, Karolina Szafranek, Rui Fernandes, Pedro Viterbo, André Sá, J. Hefty, Magdaléna Igondová, Enrique Priego-de-los-Santos, Gunnar Elgered, Magnus Lindskog, Martin Ridal, Ulrika Willén, Tong Ning, E. Bröckmann, Karina Wilgan, Alain Geiger, Çetin Mekik, Jonathan Jones, Zhizhao Liu, B. Chen, C. Wang, Salim Masoumi, Melanie Moore, Stephen Macpherson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsHistoryPolitical science

Abstract

fetched live from OpenAlex

In this section a summary of the national progress reports is given. GNSS4SWEC Management Committee (MC) members provided outline of the work conducted in their countries combining input from different partners involved. In the COST Action paticipated member from 32 COST countries, 1 Near Neighbour Country and 8 Intrantional Partners from Australia, Canada, Hong Kong and USA. The text reflects the state as of 1 January 2018.

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)0.010
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: Other
Teacher disagreement score0.216
Threshold uncertainty score0.724

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.010
Science and technology studies0.0020.001
Scholarly communication0.0070.006
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2160.203

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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