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Record W1978052418 · doi:10.1016/j.phpro.2010.08.170

Laser micromachining of optical fibre: an instrumentation enabler

2010· article· es· W1978052418 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

VenuePhysics Procedia · 2010
Typearticle
Languagees
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsInstitut National d'Optique
Fundersnot available
KeywordsInstrumentation (computer programming)Materials scienceEnablingLaserSurface micromachiningOptoelectronicsOpticsComputer scienceFabricationPhysics

Abstract

fetched live from OpenAlex

The use of lasers to process optical fibre at INO goes back in the early ’90 when a team developed a CO2 laser-based process to anneal fibre-end surface allowing the lowest back reflection-loss connectors commercially available at that time. Since then, INO has developed several processes for stripping, cleaving, polishing, end-shaping, machining, bending, welding, soldering and packaging optical fibres. More recently, INO has used laser micromachining of optical fibres in order to enable innovative instrumentation in the field of chemical sensors, flow cytometry and gas chromatography.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.044
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 communication0.0000.001
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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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