Characterizing short dispersion-length fiber via dispersive virtual reference interferometry
Bibliographic record
Abstract
The ability to characterize fibers with near-zero dispersion-length products is of considerable practical interest. We introduce dispersive virtual reference interferometry (DVRI) as a technique for the characterization of short length (<1m) fibers with near-zero disperison-length. DVRI has an accuracy equivalent to standard balanced spectral interferometry (on the order of 10(−3) ps and 10(−5) ps/nm for the group delay and dispersion-length measurements respectively) but does not require wide spectral bandwidths or multiple spectral scans. Following experimental validation, the DVRI technique is used to characterize a 23.3-cm erbium-doped gain fiber (dispersion-length product <0.002 ps/nm), using a tunable laser with a bandwidth of 145 nm. Furthermore, the dispersion in a 28.6-cm commercial dispersion shifted fiber is characterized across the zero-dispersion wavelength and the zero-disperison-wavelength and slope were determined to be 1566.7 nm and 8.57 × 10(−5) ps/(nm2∙m) with a precision of ± 0.2 nm and ± 0.06 × 10(−5) ps/(nm2∙m), 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".