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Record W4211259010 · doi:10.1017/9781108290135.007

Geomagnetic and Electromagnetic Observations at Ground Level

2019· book-chapter· en· W4211259010 on OpenAlexaff
Mioara Mandéa, Monika Korte, A. W. Yau, Eduard Petrovský

Bibliographic record

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEarth's magnetic fieldMagnetometerGeophysicsMagnetic fieldField (mathematics)Focus (optics)Point (geometry)Remote sensingGeodesyPhysicsGeologyOpticsMathematics

Abstract

fetched live from OpenAlex

In Chapter 6.1 we briefly review the main instruments used in today’s ground-based geomagnetic observations, focusing on their performances and working principles (from a user’s point of view). Next, the major measurement methods and systems currently in use will be introduced, with a focus on the latest developments in the field. In Chapter 6.2 electromagnetic (EM) methods will be discussed to study the electrical conductivity structure within Earth in a wide depth range and can be measured at the Earth’s surface by magnetometers and telluric electrodes. In Chapter 6.3 a new technique based on differences in instrument responses from ground-based magnetic measurements that extracts the frequency content of the magnetic field with periods ranging from 0.1 to 100 seconds will be discussed. This method enables the study of field line oscillations using the publicly available, worldwide database of geomagnetic observatories.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0440.028

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 head0.190
Teacher spread0.163 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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