Bibliographic record
Abstract
Recently, we found that by terminating a long length of fiber of up to 2 km with an in-fiber cavity structure, the entire structure can detect vibrations over a frequency range from 5 Hz to 100 Hz. We want to determine whether the structure (including packaging) can be optimized to detect vibrations at even higher frequencies. The structure can be used as a distributed vibration sensor mounted on large motors and other rotating machines to capture the entire frequency spectrum of the associated vibration signals, and therefore, replace the many accelerometers, which add to the maintenance cost. Similarly, it will help detect in-slot vibrations which cause intermittent contact leading to sparking under high voltages inside air-cooled generators. However, that will require the sensor to detect frequencies associated with vibration sparking, ranging from 6 kHz to 15 kHz. Then, at even higher frequencies, the structure can be useful to detect acoustic vibrations (30 kHz to 150 kHz) associated with partial discharge (PD) in generators and transformers. Detecting lower frequencies in the range 2 Hz to 200 Hz makes the sensor suitable for seismic studies and falls well into the vibrations associated with rotating machines. Another application of interest is corrosion detection in large re-enforced concrete structures by inserting the sensor along a long hole drilled around structures showing signs of corrosion. The frequency response for the proposed long-gauge vibration sensor depends on packaging.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".