MétaCan
Menu
Back to cohort
Record W2474820197 · doi:10.12943/cnr.2015.00054

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

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

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.
venuePublished in a venue whose home country is Canada.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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