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Record W1997176506 · doi:10.1063/1.1870094

Optical near-field simulation of Sb thin film thermal lens and its application in optical recording

2005· article· en· W1997176506 on OpenAlexfundno aff
Jingsong Wei, Fei Zhou, Yang Wang, Fuxi Gan, Yiqun Wu, Donghong Gu

Bibliographic record

VenueJournal of Applied Physics · 2005
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversity of Alberta
KeywordsOpticsLens (geology)EllipseMaterials scienceThermalPlane (geometry)Intensity (physics)Optical discThin filmField (mathematics)PhysicsGeometry

Abstract

fetched live from OpenAlex

Using the finite-difference-time-domain method, the near-field optical distribution and properties of Sb thin film thermal lens are calculated and simulated. The results show as follows. Within the near-field distance to the output plane of thermal lens, the spot size is approximately 100nm, and its intensity is greatly enhanced, which is higher than that of incident light. The spot shape gradually changes from ellipse to round at the distance of more than 12nm to the output plane. The above-simulated results are further demonstrated by the static optical recording experiment.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.246
Teacher spread0.236 · 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 designSimulation or modeling
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

Citations10
Published2005
Admission routes1
Has abstractyes

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