MétaCan
Menu
Back to cohort
Record W1892979955 · doi:10.1109/iqec.1998.680152

High-speed magnetic imaging

2002· article· en· W1892979955 on OpenAlexaff
M. R. Freeman, G. E. Ballentine, Wayne K. Hiebert, A. Stankiewicz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFaraday cageMagnetization dynamicsPolarization (electrochemistry)Kerr effectOpticsMagnetizationFaraday effectPhysicsSampling (signal processing)Ultrashort pulseMagnetic fieldLaser

Abstract

fetched live from OpenAlex

Summary form only given. The dynamics of the magnetization in small ferromagnetic structures is a topic of considerable current interest. The timing of this interest is largely attributable to rapid advances in magnetic recording technology, for which such dynamics will dictate the ultimate limits in speed and storage density. High-speed magneto-optic imaging is now being used in an effort to gain new insight into micromagnetic dynamics. The technique is directly analogous to electro-optic sampling of electronic systems. In magneto-optic sampling, magnetic transients are measured via small changes in the plane of polarization of linearly polarized light upon reflection from, or transmission through a sample, the Kerr and Faraday effects, respectively.) Ultrafast measurements with good signal-to-noise are obtained in pump-probe experiments in which the magnetic dynamics are driven using photoconductively switched electromagnetic circuits.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.213
Threshold uncertainty score0.713

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2130.063

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 head0.006
GPT teacher head0.228
Teacher spread0.221 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicForce Microscopy Techniques and ApplicationsFrench-language works237,207