A method of examination of liquids by neural network analysis of reflectometric time domain data from optical capillaries and fibers
Bibliographic record
Abstract
This paper presents the construction and working principles of a reflectometric intelligent fiber-optic sensor used for liquid examination. Unlike other well-known fiber optical sensors which use information from optical wavelength variations, the proposed system uses time domain data. The sensing element consists of a length of optical fiber and a short section of optical capillary and works on the reflection intensity basis. The reflected signal level depends on the optical construction of the sensor element. The changes of the monitored signal are caused mainly by variation in light propagation conditions at the interfaces of liquid and gaseous phases. The physical effects involved are Fresnel reflection, local numerical aperture variation and liquid lenses formation. It is possible to call out across the changes of those effects by introducing a measuring procedure which includes submerging, submersion, emerging and emergence of the sensing head from the examined liquid or by local heating of the liquid sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".