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Record W4367672476 · doi:10.1364/ao.486929

Towards real-time active imaging of greenhouse gases using tunable mid-infrared all-fiber lasers

2023· article· en· W4367672476 on OpenAlexafffundabout
Louis-Charles Michaud, Tommy Boilard, Sébastien Magnan-Saucier, Pascal Paradis, Lauris Talbot, Antoine Thiboult, Daniel F. Nadeau, Réal Vallée, Martin Bernier

Bibliographic record

VenueApplied Optics · 2023
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundCanada Foundation for Innovation
KeywordsMaterials scienceFiber Bragg gratingFiber laserOpticsLaserOptoelectronicsNarrowbandFiberTunable laserWavelengthPhysics

Abstract

fetched live from OpenAlex

We report a tunable all-fiber laser emitting a maximum output power of 2.55 W around 3240 nm. The fiber laser cavity based on a fluoride fiber doped with dysprosium ions yields an efficiency of 42% according to the in-band launched pump power at 2825 nm. Due to a custom piezoelectric fiber Bragg grating (FBG) package, mechanical strains applied to the narrowband FBG used as the input cavity coupler allowed for fast tuning of the emission wavelength over a spectral range of 1.5 nm. This laser was deployed in the field in northern Québec (Canada) to assess its performances for remote sensing of methane in the presence of a significant amount of water vapor, i.e., over a hydroelectric reservoir. The preliminary results acquired during this field campaign confirm the great potential of the proposed approach for the development of a real-time active imaging system of greenhouse gases.

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 categoriesInsufficient payload (model declined to judge)
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 score0.192
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.280
Teacher spread0.260 · 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.

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

Citations15
Published2023
Admission routes3
Has abstractyes

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