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Record W3080139898 · doi:10.1117/12.2567138

Optical tweezer path towards single photon sources at low-loss fiber wavelengths

2020· article· en· W3080139898 on OpenAlexaff
Amirhossein Alizadehkhaledi, Adriaan L. Frencken, Frank C. J. M. van Veggel, Reuven Gordon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOptoelectronicsOptical tweezersOptical fiberWavelengthPhotonCoupling (piping)OpticsMaterials scienceCommon emitterSingle-photon sourcePlastic optical fiberQuantum dotPhysicsFiber optic sensor

Abstract

fetched live from OpenAlex

Single photon sources are desired for quantum computing and communication applications. Ideally, these sources would provide single photons on demand at the low-loss fiber optical communication wavelength of 1550 nm. Single Erbium ions provide a good candidate for such sources because they emit at the right wavelength and they are very stable, however, the challenges remain of isolating a single emitter, coupling its emission to optical fiber and enhancing its emission rate. Nanoaperture optical tweezers provide a pathway to solving these issues by trapping and identifying single emitters, enhancing their emission rate and providing efficient coupling to an optical fiber. Recently, we have made progress in each of these areas (trapping, enhancing and coupling), which will be reviewed in this talk.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.004

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 head0.014
GPT teacher head0.228
Teacher spread0.213 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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