MétaCan
Menu
Back to cohort
Record W2063582606 · doi:10.1117/12.841277

Programmable lasers: design and applications

2010· article· en· W2063582606 on OpenAlexaff
Bryan Burgoyne, A. Villeneuve

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsGenia Photonics (Canada)
Fundersnot available
KeywordsLaser linewidthLaserOpticsPulse durationFiber laserPicosecondMaterials scienceLaser power scalingAmplifierWavelengthSweep frequency response analysisOptoelectronicsComputer sciencePhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

A rapidly tunable, electronically controlled, pulse duration adjustable, arbitrarily programmable wavelength, picosecond mode-locked fiber laser is presented. The laser is tunable over 80 nm with sweeping frequency over 10 million wavelengths per second. The user can select from a preset linear sweep in either wavelength or optical frequency (kspace) or a custom (user-defined) sweep. Pulse duration is adjustable over tens of picoseconds with nearly Fourier limited linewidth. The laser can be harmonically mode-locked over 1 GHz. The average power is again fully programmable and is at least 50 mW, Watt level is possible with a high power amplifier. The output is a single mode polarization maintaining fiber. The laser possesses several external triggers, such as one trigger per optical pulse, one delayed trigger per optical pulse to synchronize with the experiments, one at the beginning when the laser is ready to sweep to start the data acquisition and one for each consecutive sweep, and finally one trigger for each wavelength change. Such a laser is so versatile that it can be used for medical imaging, material machining and nonlinear optics. It proves also a valuable research tool since all the parameters are adjustable.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.586
Threshold uncertainty score0.778

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 science0.0010.000
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Laser TechnologiesFrench-language works237,207