Localized temperature and pressure measurements inside CS<sub>2</sub>-filled fiber using stimulated Brillouin scattering
Bibliographic record
Abstract
Liquid-core fibers are a versatile platform for nonlinear light-matter interactions, combining highly nonlinear materials like carbon disulfide (CS 2 ) or nitrobenzene with the confinement and long interaction length of optical fibers. While there is only minor influence of strain inside the fiber core due to the liquid phase, pressure effects have a dominant role depending on the thermodynamic regime and filling of the fiber core [1] . In our experiments we use Brillouin Optical Correlation Domain Analysis (BOCDA) to create a localized Brillouin response [2] . Due to the associated refractive index change, this response is strain, pressure and temperature dependent. While the discrimination of strain and temperature effects in silica fibers is complicated due to the combined Brillouin response [3] , CS 2 filled fibers have the advantage of thermo- and piezo-optical coefficients with different signs. While the thermo-optic coefficient is negative, resulting in a downshifted acoustic response for higher temperature, the piezo-optic coefficient is positive, resulting in an upshifted Brillouin frequency for increased pressure. To investigate the response of the CS 2 , we heated different parts of the 60cm liquid-core fiber on a hotplate up to a temperature of 135 ◦ C while measuring the distributed Brillouin response with a resolution of 4cm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".