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Record W2024996067 · doi:10.1142/s0129156408005345

SIGNAL PROCESSING OF MULTICOMPONENT RAMAN SPECTRA OF PARTICULATE MATTER

2008· article· en· W2024996067 on OpenAlex
Javier Fochesatto, J. J. Sloan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of High Speed Electronics and Systems · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Waterloo
FundersArmy Research LaboratoryNatural Sciences and Engineering Research Council of CanadaU.S. Air Force
KeywordsRaman spectroscopyParticulatesSIGNAL (programming language)Environmental scienceSignal processingIdentification (biology)Remote sensingEnvironmental chemistryAnalytical Chemistry (journal)Computer scienceChemistryPhysicsTelecommunicationsGeographyOptics

Abstract

fetched live from OpenAlex

We report advances in the signal processing of Multicomponent Raman Spectra of particulate matter. We evaluate laboratory and ambient samples collected in field experiments in Canada (during the Pacific 2001 Experiment, Vancouver, BC and at ALERT station, Nunavut, 2002). We discuss methodologies for signal processing the Raman spectra: de-noising and de-peaking, baseline reduction, and identification of chemical fingerprints. The ambient samples were collected near the surface in different environmental conditions during field experiments. In this article we compare and assess the methodologies performances and differences.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.268

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 head0.012
GPT teacher head0.295
Teacher spread0.283 · 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