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Record W2947516949 · doi:10.3390/condmat4020050

Surface Plasmon Resonance (SPR) to Magneto-Optic SPR

2019· article· en· W2947516949 on OpenAlexafffund
Conrad Rizal, V. I. Belotelov, Daria O. Ignatyeva, А. К. Звездин, Simone Pisana

Bibliographic record

VenueCondensed Matter · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsYork University
FundersMitacs
KeywordsSurface plasmon resonanceSoftware portabilityNanotechnologyMagnetoComputer scienceSensitivity (control systems)Materials scienceEngineeringElectronic engineeringElectrical engineeringNanoparticle

Abstract

fetched live from OpenAlex

In this editorial, a brief background of the surface plasmon resonance (SPR) principle is discussed, followed by several aspects of magneto-optic SPR (MOSPR) and sensing schemes from the viewpoint of fundamental studies and potential technological applications. New sensitivity metrics are introduced that would allow researchers to compare the performance of SPR and MOSPR-based sensors. Merits of MOSPR over SPR based sensors and challenges faced by MOSPR sensors in terms of their practical use and portability are also considered. The editorial ends with potential new configurations and future prospects. This work is considered highly significant to device engineers, graduate and undergraduate students, and researchers of all levels involved in developing new classes of bio-devices for sensing, imaging, environmental monitoring, toxic gas detection, and surveying applications to name a few.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.005

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 head0.010
GPT teacher head0.226
Teacher spread0.215 · 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

Citations33
Published2019
Admission routes2
Has abstractyes

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