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Record W4319596620 · doi:10.1063/5.0132123.2

10.1063/5.0132123.2

2023· dataset· en· W4319596620 on OpenAlexaff
Yifan Liu, Panpan Yu, Yijing Wu, Ziqiang Wang, Yinmei Li, Jinyang Liang, Puxiang Lai, Lei Gong

Bibliographic record

VenueDefault Digital Object Group · 2023
Typedataset
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMulti-mode optical fiberSingle shotFrame rateReflection (computer programming)OpticsComputer scienceDigital micromirror deviceComputer visionArtificial intelligenceEndoscopeChannel (broadcasting)Optical fiberPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Yifan Liu, Panpan Yu, Yijing Wu, Ziqiang Wang, Yinmei Li, Jinyang Liang, Puxiang Lai, Lei Gong; Single-shot wide-field imaging in reflection by using a single multimode fiber. Appl. Phys. Lett. 6 February 2023; 122 (6): 063701. https://doi.org/10.1063/5.0132123 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioApplied Physics Letters Search Advanced Search |Citation Search

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.431
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.5690.783

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 spread0.216 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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