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Record W4416393870 · doi:10.32604/cmc.2025.066421

Error Analysis of Geomagnetic Field Reconstruction Model Using Negative Learning for Seismic Anomaly Detection

2025· article· en· W4416393870 on OpenAlexfundno aff
Nur Syaiful Afrizal, Khairul Adib Yusof, Lokman Hakim Muhamad, Nurul Shazana Abdul Hamid, Mardina Abdullah, Mohd Amiruddin Abd Rahman, Syamsiah Mashohor, Hayakawa Masashi

Bibliographic record

VenueComputers, materials & continua/Computers, materials & continua (Print) · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
FundersHelmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZUniversiti Putra MalaysiaSveriges Geologiska UndersökningFlorida Institute of TechnologyAlberta Agricultural Research Institute
KeywordsEarth's magnetic fieldSensitivity (control systems)Anomaly detectionAnomaly (physics)Field (mathematics)Pattern recognition (psychology)Magnetic anomaly

Abstract

fetched live from OpenAlex

Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years. This study introduces a novel reconstruction-based modeling approach ... | Find, read and cite all the research you need on Tech Science Press

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)0.005
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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