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Record W2175022800 · doi:10.1175/jtech1863.1

Calibrating the Spatial Response of Bio-Optical Sensors

2006· article· en· W2175022800 on OpenAlexaff
Fabian Wolk, Hidekatsu Yamazaki, Hua Li, Rolf G. Lueck

Bibliographic record

VenueJournal of Atmospheric and Oceanic Technology · 2006
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Victoria
FundersJapan Society for the Promotion of Science
KeywordsFluorometerWavenumberTransfer functionRemote sensingOpticsSampling (signal processing)Image resolutionWavelengthBiological systemFluorescenceMaterials sciencePhysicsDetectorGeology

Abstract

fetched live from OpenAlex

Abstract This article describes an experimental method used to establish the spatial wavenumber response of in situ fluorometers. The method is applied to a fluorometer developed to measure the structure of the fluorescence field at high spatial wavenumbers. This fluorometer detects fluorescence variations on centimeter scales by creating a sampling volume in the undisturbed flow region, outside of the sensor housing. The sampling volume is created by intersecting beams of blue excitation light. To establish the size of the sampling volume and the amount of spatial averaging, the fluorometer and a fast response thermistor are towed repeatedly through a warm, fluorescent plume in a tow tank. The ratio of the measured fluorescence and temperature spectrum determines the wavenumber response of the fluorometer. The measured spectral ratio is well described by the transfer function of a first-order, low-pass filter with a half-power point at 22 cpm. The equivalent spatial resolution is 7 mm. The transfer function model can be used to correct measured fluorescence spectra for the limited wavenumber response of the sensor.

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)0.001
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 score0.097
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.004
GPT teacher head0.195
Teacher spread0.192 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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