100-km sensing range Brillouin optical time domain analysis based on time-division multiplexing
Bibliographic record
Abstract
We propose and demonstrate a novel high-performance 100-km range Brillouin optical time-domain analysis (BOTDA) based on time-division multiplexing. To avoid the limitation of pump depletion or excess amplification in a conventional long-range BOTDA, in our scheme instead of a pulse and a CW wave but two pulses, i. e., a probe pulse and a pump pulse are used to perform the measurement. The delay between the probe and pump pulses can be changed to select the sensing section where the probe pulse interacts with the pump pulse. The measurement of the entire sensing fiber is realized by implementing the measurement for each sensing section through changing the delay between the two pulses. In experiment, a 100-km sensing fiber is divided into 11 sections based on gain-controlled principle, and a spatial resolution of 0.6 m and 2 m are obtained at the end of 75 km and 100 km, respectively.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".