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Record W2599152240 · doi:10.15353/vsnl.v2i1.97

Enhanced Smartphone Spectroscopy via High-throughput Computational Slit

2016· article· en· W2599152240 on OpenAlexfundvenueno aff
Emily Kuang, Farnoud Kazemzadeh, Alexander Wong

Bibliographic record

VenueJournal of Computational Vision and Imaging Systems · 2016
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsnot available
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSoftware portabilitySpectrometerLimitingThroughputSpectroscopyComputer scienceSpectral resolutionSignal-to-noise ratio (imaging)Noise (video)SlitOpticsMaterials scienceSpectral linePhysicsEngineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

High-performance spectroscopy is often limited by its portability, size, and cost, therefore limiting its reach into various applications that may benefit from it. In this paper, we present a low-cost, low-complexity slitless smartphone-based spectrometer that can be useful for carrying out field studies. Omitting a slit in a spectrometer means loss of spectral resolution in conventional spectrographs; however, we overcome this limitation via the use of a high-throughput computational slit to produce spectra with enhanced spectral resolution and enhanced signal-to-noise characteristics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.714
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.266
Teacher spread0.261 · 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 teacher head, 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

Citations1
Published2016
Admission routes2
Has abstractyes

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