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Record W2036721283 · doi:10.1364/ao.49.004883

Predictive analog-to-digital converter for Fourier-transform spectrometers

2010· article· en· W2036721283 on OpenAlexaff
Jean-Daniel Deschênes, Simon Potvin, Jean-Simon Ash, Jérôme Genest

Bibliographic record

VenueApplied Optics · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConvertersComputer scienceDynamic rangeFourier transformSpectrometerNoise (video)Electronic engineeringOpticsArtificial intelligencePhysicsPower (physics)EngineeringComputer vision

Abstract

fetched live from OpenAlex

This paper proposes the use of predictive analog-to-digital converters (ADC) to handle dynamic range issues in Fourier-transform spectrometers. Several predictive approaches are proposed, and one is implemented experimentally to show that the technique works. A system was implemented with 16 bit (13 bits effective) ADCs and digital-to-analog converters (DACs) operated at 8 bits to provide a comparison basis. Measurements of a blackbody at 900 °C performed using the setup show a 13 bit effective performance, limited by the input noise of the data acquisition card.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.565

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 head0.006
GPT teacher head0.222
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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2010
Admission routes1
Has abstractyes

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