CREC Fiber-Optic Probes for Particle Velocity and Particle Clustering: Design Principles and Calibration Procedures
Bibliographic record
Abstract
The present study describes the novel CREC-GS-Optiprobe design. This new sensor allows for the characterization of clustering phenomena in gas−solid fluidized beds with minimum intrusion effects. This sensor is composed of the following components and functionalities: (a) an optical fiber acts as a laser beam emitter; (b) a GRIN (graded-refractive-index) lens, separated from the emitter fiber at a selected distance, creates a region of high light intensity far from the tip of the probe; and (c) a second optical fiber, placed adjacent to the GRIN lens, acts as a receiver to collect the light rays reflected by the moving particles crossing the region of high light intensity. The GRIN lens forms a 285-μm-diameter region of high light intensity at a distance of 5.4 mm from the tip of the sensor. The equations used in the design of the CREC-GS-Optiprobe were derived assuming paraxial optics. The calibration methods that are reported in this study were designed to improve probe performance. Reflection tests demonstrated that the probe develops a region of high light intensity where the beam converges into a waist. After probe calibration and alignment, the acquired signals display distinctive peaks, showing the simultaneous detection of particle clusters on both probes.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".