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Record W4393970846 · doi:10.1002/9781119678892.ch3

LIGHT SOURCES

2024· other· en· W4393970846 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

Venuenot available
Typeother
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsDouglas Mental Health University Institute
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

This chapter reviews some of the fundamental properties of light sources that are of particular importance to fiber optic sensors. It describes the various types of light sources as well as their output characteristics. Light sources used to support fiber optic sensors produce light that is often dominated by either spontaneous or stimulated emission. Efforts are made to avoid the recirculation of light in the optical cavity, so the resulting single-pass output beam has low coherence, similar to a light source dominated by spontaneous emission. When two light beams are combined, they will interfere with each other in a constructive or destructive manner provided that their relative phases are well defined. For most fiber optic sensors, semiconductor-based light sources offer advantages in power consumption, reliability, size, and cost that often preclude the selection of alternative light sources. The chapter reviews some of the most commonly used semiconductor light sources for fiber sensor applications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.989

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)0.0120.036

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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