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Record W1983436361 · doi:10.1117/12.840842

Picosecond laser patterning of NiCr thin film strain gages

2010· article· en· W1983436361 on OpenAlex

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.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceStrain gaugeNichromeThin filmFluenceComposite materialSubstrate (aquarium)PicosecondOptoelectronicsMicroheaterStrain (injury)OpticsLaserNanotechnologyFabrication

Abstract

fetched live from OpenAlex

This paper presents results of ablation experiments of NiCr layers with thicknesses ranging from 23nm to 246nm on Al<sub>2</sub>O<sub>3</sub> substrates. Investigated parameters are fluence, number of pulses, film thickness and substrate roughness. The influence of the parameters on the removal threshold is analyzed in order to identify stable processing parameters. Patterned NiCr thin films as an essential component for the measurement of mechanical stress are required for the development of sputtered thin film strain gages. With this new approach strain sensors will be resistant against creeping or swelling through changing ambient conditions unlike conventional strain gages.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.243
Teacher spread0.231 · 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