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Record W2051496147 · doi:10.1088/0026-1394/44/2/006

Two procedures for the estimation of the uncertainty of spectral irradiance measurement for UV source calibration

2007· article· en· W2051496147 on OpenAlexfundno aff
Jessica Lebenberg, Nicolas Fischer, Séverine Guimier, J. Dubard

Bibliographic record

VenueMetrologia · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsIrradianceMonte Carlo methodCalibrationPropagation of uncertaintyTaylor seriesFeature (linguistics)Computer scienceProbability density functionFunction (biology)Measurement uncertaintyStatistical physicsAlgorithmOpticsMathematicsStatisticsPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

The measurement uncertainty of the spectral irradiance of an UV lamp is computed by using the law of propagation of uncertainty (LPU) as described in the ‘ Guide to the Expression of Uncertainty in Measurement ’ (GUM), considering only a first-order Taylor series approximation. Since the spectral irradiance model displays a non-linear feature and since an asymmetric probability density function (PDF) is assigned to some input quantities, the usage of another process was required to validate the LPU method. The propagation of distributions using Monte Carlo (MC) simulations, as depicted in the supplement of the GUM (GUM-S1), was found to be a relevant alternative solution. The validation of the LPU method by the MC method is discussed with regard to PDF choices, and the benefit of the MC method over the LPU method is illustrated.

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)0.011
metaresearch head score (Gemma)0.068
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0090.004
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.217
GPT teacher head0.400
Teacher spread0.183 · 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
GenreMethods

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

Citations9
Published2007
Admission routes1
Has abstractyes

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