Self-Healing Dynamically Controllable Micro-Comb
Bibliographic record
Abstract
Micro-resonator-based frequency combs, or micro-combs, have gained considerable interest in recent years due to their many potential applications such as high-speed communication systems, spectroscopy and ultrafast optical clocks [1]. Most micro-combs systems are based on laser pumped optical parametric oscillation and are typically non-self-starting, requiring a well-defined warm-up strategy involving smart control [2]. An alternative approach to micro-combs is represented by the Filter-Driven Four-Wave Mixing (FD-FWM) laser [3-5], based on a nonlinear micro-resonator nested in a main amplifying fibre cavity. Although this system has demonstrated self-starting regimes, stable operation typically imposes a strict relation between the minimum free-spectral range (FSR) of the main-cavity and the Q-factor of the micro-resonator. The use of longer main-cavity fibre lengths (highly desirable for several positive features, such as a larger gain) results in unrecoverable unstable regimes, i.e. in super-mode instability, which arises from the existence of many oscillating main-cavity modes within each micro-resonator resonance.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".