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Record W3004887703 · doi:10.1515/nanoph-2019-0532

Proceedings of the 9th International Conference on Surface Plasmon Photonics (SPP9)

2020· article· en· W3004887703 on OpenAlexaff
N. Asger Mortensen, Pierre Berini, Uriel Levy, Sergey I. Bozhevolnyi

Bibliographic record

VenueNanophotonics · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
FundersNovo Nordisk FondenFonds National de la Recherche LuxembourgDanmarks GrundforskningsfondVillum Fonden
KeywordsPhotonicsNanomaterialsSurface plasmonNanotechnologyPlasmonMaterials scienceEngineering physicsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The research field of plasmonics is concerned with the interaction of light with free electrons in conducting media, thus, having a natural emphasis on metal nanostructures while now also being explored in several other novel material systems ranging from macromolecules and two-dimensional materials (such as graphene) to doped semiconductors. The field of plasmonics is bridging fundamental research and diverse applications, embracing traditional topics such as sensing as well as emerging ones such as localized heating and hot-electron generation. The synergy of light with nanotechnology is opening a range of application areas important to society. After two decades of explosive growth, plasmonics is still going strong: according to “2019 Research Fronts”, the topic “Plasmonic properties of metal nanostructures” belongs to the top 10 research fronts in physics [1].

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: Other
Teacher disagreement score0.078
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0780.049

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 head0.239
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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