<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mmultiscripts><mml:mi mathvariant="normal">Sr</mml:mi><mml:mprescripts/><mml:none/><mml:mn>88</mml:mn></mml:mmultiscripts><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:mrow></mml:math>single-ion optical clock with a stability approaching the quantum projection noise limit
Bibliographic record
Abstract
A number of optical frequency standards have been evaluated with fractional uncertainties between ${10}^{\ensuremath{-}17}$ and ${10}^{\ensuremath{-}18}$. Reduction of the statistical uncertainty of a clock comparison to this level is challenging, requiring the best possible stability to avoid excessively long averaging times. We report recent improvements of our $^{88}\mathrm{Sr}{}^{+}$ single-ion standard that have reduced its 1-s Allan deviation from $1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}14}$ to $3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}15}$, yielding an order of magnitude decrease in averaging time for a given statistical uncertainty level. Among the improvements made are the implementation of a clear-out laser that transfers the ion from the metastable state to the ground state at each cycle, followed by a state preparation step that transfers the ion to the ground-state magnetic sublevel of the probed transition. With these changes, the ion clock transition interacts with the probe laser at every interrogation cycle. The stability of our optical standard is essentially limited by the quantum projection noise for pulse lengths up to $\ensuremath{\approx}100$ ms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.420 | 0.351 |
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".