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Record W4385574845 · doi:10.1109/mim.2023.10208249

Simple Offset Elimination Technique for Two-Wire Measurements

2023· article· en· W4385574845 on OpenAlex
M.S. Obrecht

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Instrumentation & Measurement Magazine · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Electrical Measurement Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLCR meterInductorCapacitorTest fixtureCapacitanceElectrical impedanceInductanceCapacitive sensingMaterials scienceParasitic capacitanceElectrical engineeringEquivalent series resistanceAcousticsParasitic elementOptoelectronicsElectronic engineeringEngineeringVoltagePhysicsElectrode

Abstract

fetched live from OpenAlex

Measuring small inductors and capacitors can be challenging with the use of conventional LCR-meters that have a test frequency of 10 kHz or less. With a 10 nH inductor at 10 kHz, the impedance is only 6 mOhms, that is comparable to the resistance of the probes. At a frequency of 100 kHz, the impedance increases to 60 mOhms. On the other hand, a 1 pF capacitor at 10 kHz results in an impedance of 15 MOhms, which makes a capacitive connection between the probes noticeable and affects the measurement of impedance. This paper presents two case studies: an extraction the parasitic inductance of the two-wire probes using the HP4284A LCR-meter and HP16034E test fixture, and extraction of the parasitic capacitance using the LCR-Reader-R2 tweezer-meter. This method enables accurate measurements of sub-nH inductors and sub-pF capacitors using test frequencies below 300 kHz.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.307
Teacher spread0.244 · 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