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Record W2029876577 · doi:10.1190/1.2370409

Guidelines for location and use of base station data in aeromagnetic survey processing

2006· article· en· W2029876577 on OpenAlexaffabout
Marc A. Vallée, James A. Craven, Larry Newitt, Pierre Keating, Régis Dumont, Ian J. Ferguson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of ManitobaGeological Survey of Canada
Fundersnot available
KeywordsAeromagnetic surveyData processingBase (topology)Computer scienceBase stationRemote sensingGeodesyGeologyDatabaseTelecommunications

Abstract

fetched live from OpenAlex

Observations have indicated that temporal variations in the magnitude of the geomagnetic field can show a significant decrease in correlation at distances larger than 200 km. We confirm this by a study of geomagnetic measurements from a magneto‐telluric survey in Alberta, Canada. Using these data, we estimate the coherence of the total‐field magnetic signal between pairs of stations as a function of distance. The measurements are from two profiles, one where a strong conductor is present at depth and one where the conductivity is laterally uniform over the penetration depth of the signals. The coherence decreases at short periods and with increasing distance between the stations. Different coherence behavior is observed for the two profiles. This is attributed to the differences in the geo‐electric sections. These results are used to propose improved guidelines for the use of base‐station measurements in aeromagnetic data processing.

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)0.035
metaresearch head score (Gemma)0.140
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.035
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.140
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.011
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0060.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.016

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 head0.193
GPT teacher head0.373
Teacher spread0.180 · 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
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

Citations2
Published2006
Admission routes2
Has abstractyes

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