Bibliographic record
Abstract
In this talk, I will present a basic review of atomic, molecular and atom-like sensors. Topics of interest are RF electric field sensors, magnetometers, gyroscopes and clocks. Atom and Molecule-based sensors have attracted a lot of attention recently because the quantum system used for the sensor is given to us by nature. The universality of atoms and molecules can be used to make these sensors stable and accurate. Atom-like sensors resemble atoms and molecules because they have discrete quantum states comprising optically active electrons. Examples of atom-like sensors are defect centers and quantum dots. Atom-like sensors can be manmade, i.e., quantum dots. Manmade sensors can be difficult to make with the necessary uniformity for many applications, but the ability to engineer the properties of the elementary system is beneficial. Quantum systems in the solid state that resemble atoms, like defect centers, can be robust and possess high spatial resolution, but are complicated by the fact that their properties generally depend on their environment. Themes, such as optical readout and preparation will be addressed. Optical readout and preparation are important because there is little noise in the electromagnetic field at optical frequencies and laser fields can be used to change the properties of the atomic sensors. Commercialization of atomic sensors and its challenges will be briefly covered.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".