Bibliographic record
Abstract
Frequency fluctuations of an inexpensive single-mode semiconductor diode laser, which operates in the 822 nm region, are investigated by direct measurement of the error signal. The linear slope of first derivative signal of a transition in the (2,1,1) vibration-rotation band of water vapour is used as a frequency discriminator. A balanced photodetector is used to reduce the intensity noise and to improve the S/N ratio. Frequency stability of the diode laser is investigated when the laser is under a free-running condition and is locked to the line center of the reference transition. An integrator is used to provide feedback voltage to the laser current driver. After frequency stabilization, a more than 60-fold improvement in long-term laser-frequency stability is attained over the performance provided by the free-running semiconductor diode laser. The frequency-noise power spectrum of the diode laser is extracted from the error signal for the Fourier-frequency range ~100 Hz. The Allan variance curve for the laser system is obtained from the frequency-noise power spectrum of the error signal by using a suitable mathematical relation under certain approximations. The extracted values of the Allan variance are compared with the theoretical τ –1 model. The experimental setup is easy to implement in graduate laboratory classes. PACS Nos.: 42.55.Px, 42.62.Fi, 33.70.Jg
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 | 0.001 |
| 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) | 0.001 | 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 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".