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Record W1741617303 · doi:10.1109/ccece.1993.332268

Implementation of a multi-channel biomagnetic measurement system using DSP technology

2002· article· en· W1741617303 on OpenAlexafffund
Joseph C. McKay, Jan Vrba, K. Betts, M. B. Burbank, S. Lee, Keiichiro Mori, D. Nonis, P. D. Spear, Y. Uriel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsCoquitlam College
FundersMinistère de la Défense Nationale
KeywordsDigital signal processingComputer scienceLinearizationChannel (broadcasting)HarmonicsNoise (video)Signal processingFilter (signal processing)Computer hardwareEmbedded systemEngineeringArtificial intelligenceElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Reports on the functions, architecture, and performance of the digital signal processing components of a biomagnetometer system including over 100 total channels. The functions of the system include sensor linearization via feedback, sample rate conversion filtering, noise cancellation, variable cutoff low and high pass filtering, and notch or comb filtering for power line harmonics. The flexible, high performance system architecture is based on commercial DSP chips and a mixture of serial and parallel data communications paths. The overall performance of the first production system is summarized.>

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.052
GPT teacher head0.252
Teacher spread0.201 · 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 designBench or experimental
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

Citations7
Published2002
Admission routes2
Has abstractyes

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