Pseudo-circulator implemented as a multimode fiber coupler
Bibliographic record
Abstract
We present a linear all-fiber device exhibiting the functionality of a circulator, albeit for multimode fibers. We define a pseudo-circulator as a linear three-port component that transfers most of a multimode light signal from Port 1 to Port 2, and from Port 2 to Port 3. Unlike a traditional circulator which depends on a nonlinear phenomenon to achieve a non-reciprocal behavior, our device is a linear component that seemingly breaks the principle of reciprocity by exploiting the variations of etendue of the multimode fibers in the coupler. The pseudo-circulator is implemented as a 2x2 asymmetric multimode fiber coupler, fabricated using the fusion-tapering technique. The coupler is asymmetric in its transverse fused section. The two multimode fibers differ in area, thus favoring the transfer of light from the smaller to the bigger fiber. The desired difference of area is obtained by tapering one of the fiber before the fusion process. Using this technique, we have successfully fabricated a pseudo-circulator surpassing in efficiency a 50/50 beam-splitter. In all the visible and near-IR spectrum, the transmission ratio exceeds 77% from Port 1 to Port 2, and 80% from Port 2 to Port 3. The excess loss is less than 0.5 dB, regardless of the entry port.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".