MétaCan
Menu
Back to cohort
Record W4416254786 · doi:10.48550/arxiv.2509.19179

Who Let the Diamonds Out?

2025· preprint· en· W4416254786 on OpenAlexaboutno aff
Vincent Halde, Olivier Bernard, Mathieu Brochu, Laurier Dufresne, Nicolas Fleury, Kayla Johnson, Benjamin C. Moffett, David Roy-Guay

Bibliographic record

VenueArXiv.org · 2025
Typepreprint
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetometerSoftware deploymentRobustness (evolution)Software portabilityImplementationSensitivity (control systems)

Abstract

fetched live from OpenAlex

Nitrogen-Vacancy (NV) center magnetometry is a highly promising quantum sensing technology, with early prototypes demonstrating impressive sensitivity in compact sensing heads. Yet, most existing implementations remain tied to laboratory setups, lacking the portability and environmental robustness needed to unlock their full potential in real-world applications. In this work, we introduce a fully portable, hand-held NV-based magnetometer that delivers a vector sensitivity of approximately 400 pT/sqrt(Hz), heading errors below 5 nT in Earth's field, and a wide signal bandwidth that supports on-field recalibration and operation on moving platforms. We further demonstrate the system's technological maturity through environmental qualification such as thermal, vibration, radiation and other operational stresses related to deployment in low Earth orbit, and through successful deployments in demanding scenarios, including northern Canadian weather conditions, drone-mounted surveys and high-altitude balloon flights. Together, these achievements establish this NV-based magnetometer as a robust, versatile tool ready to bring quantum sensing performance to a broad range of field and autonomous applications.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.971

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 science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.251
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueArXiv.orgSame topicMagnetic Field Sensors TechniquesFrench-language works237,207