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Record W4394498729 · doi:10.6084/m9.figshare.16589867

Data for Slow Light Nanocoatings for Ultrashort Pulse Compression

2023· dataset· en· W4394498729 on OpenAlexaboutno aff
Marcus Ossiander

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceUltrashort pulseCompression (physics)Pulse compressionPulse (music)OptoelectronicsOpticsComputer scienceComposite materialPhysicsTelecommunicationsLaser

Abstract

fetched live from OpenAlex

Data for Slow light nanocoatings for ultrashort pulse compression M. Ossiander1,* Y.-W. Huang1,2, W. T. Chen1, Z. Wang3, X. Yin1, Y. A. Ibrahim1,4, M. Schultze3, F. Capasso1*1 John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St, Cambridge, MA 02138, USA. 2 Department of Photonics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan. 3 Institute of Experimental Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria. 4 University of Waterloo, Waterloo, ON N2L 3G1, Canada. * Corresponding Authors: mossiander@g.harvard.edu, capasso@seas.harvard.edu Manuscript published in Nature Communications 12, 6518 (2021), https://www.nature.com/articles/s41467-021-26920-6Details of the data files are described below and in the description.txt files. Methods are described in the publication.-- Folder frog_data: This folder contains the frequency resolved optical gating data for the 162 nm nanopillar diameter coating. Reference data for the incoming femtosecond laser pulses and the incoming laser pulses transmitted through the uncoated substrate only are also provided.The variables in the files are: wavelength_nm: wavelength axis for frequency resolved optical gating spectrogram in nanometers. delay_fs: delay axis for the frequency resolved optical gating spectrogram in femtoseconds. spectrogram: the raw frequency resolved optical gating spectrogram in arbitrary units.Folder white_light_data: This folder contains the raw white light interferometer data. Folders are named according to the nanopillar diameter of the examined compressor coating. For each coating, reference data of the empty interferometer, and reference data of the substrate in one interferometer arm (double pass) is also provided.The variables in the files are: wavelength: wavelength axis for the recorded spectral intensities in nanometers. background: Spectral intensity after the interferometer with both interferometer arms blocked in arbitrary units. A: Spectral intensity after the interferometer with sample arm blocked in arbitrary units. B: Spectral intensity after the interferometer with reference arm blocked in arbitrary units. AB: Spectral intensity after the interferometer in arbitrary units. wavelengthGD: wavelength axis for the calculated group delay in nanometers. frequencyGD: frequency axis for the calculated group delay in petahertz. GDnm: calculated group delay in femtoseconds (plot versus wavelengthGD). GDPHz: calculated group delay in femtoseconds (plot versus frequencyGD).

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.268
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2680.065

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 head0.078
GPT teacher head0.349
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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