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Record W2907322778 · doi:10.1002/cmr.b.21401

A low‐cost multi‐channel software‐defined radio‐based NMR spectrometer and ultra‐affordable digital pulse programmer

2018· article· en· W2907322778 on OpenAlexafffund
Carl A. Michal

Bibliographic record

VenueConcepts in Magnetic Resonance Part B · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFirmwareProgrammerSoftwareSoftware-defined radioMicrocontrollerInstrumentation (computer programming)Computer scienceSpectrometerSynchronization (alternating current)Open source hardwareComputer hardwareSynchronizingLaptopController (irrigation)Embedded systemChannel (broadcasting)Open sourceTelecommunicationsOperating systemPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract In recent years, a new generation of software‐defined radio (SDR) products has emerged that are well suited for adaptation for NMR instrumentation. The software and hardware of a new NMR spectrometer console based upon an inexpensive SDR product are described. This device is provided with open‐source firmware and drivers which have allowed the customization of its behavior and optimization for use in NMR. In particular, these low‐level modifications have allowed for straightforward synchronization of multiple SDR boards with each other and with other devices. Control of other devices is facilitated by a new digital pulse programmer constructed using an inexpensive open‐source, widely available microcontroller platform. The strategy used for precise, reproducible synchronization of the different components is described in detail. Representative spectra are presented demonstrating both high‐resolution and wide‐line spectroscopy with spectral widths up to 50 MHz.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.014
GPT teacher head0.306
Teacher spread0.291 · 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
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

Citations30
Published2018
Admission routes2
Has abstractyes

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