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Record W2474820197 · doi:10.12943/cnr.2015.00054

ON-LINE IRRADIATION TESTING OF A GIANT MAGNETO-RESISTIVE (GMR) SENSOR

2016· article· en· W2474820197 on OpenAlexaffvenue
Jeffrey Olfert, Brian Luloff, Dan MacDonald, R. D. Lumsden

Bibliographic record

VenueCNL Nuclear Review · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsResistive touchscreenMaterials scienceMagnetoIrradiationSensitivity (control systems)OptoelectronicsMagnetic fieldElectrical engineeringElectronic engineeringMagnetEngineeringPhysics

Abstract

fetched live from OpenAlex

Magneto-resistive sensors are rapidly gaining favour for magnetic field sensing applications owing to their high sensitivity, small size, and low cost. Their metallic, nonsemiconductor construction makes them excellent candidates for use in the harsh environments present in nuclear and space applications. In this work, a commercially available magneto-resistive sensor was irradiated up to a total gamma dose of 2 MGy (200 Mrad), and online testing was performed to monitor the sensor throughout the irradiation to detect any degradation. No significant evidence of degradation of the sensor characteristics was observed. A very small (< 1%) change in the bridge balance of the sensor as a function of accumulated dose was detected.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.274
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

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

Citations2
Published2016
Admission routes2
Has abstractyes

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