Bibliographic record
Abstract
In this paper , the basic principle,application,progress in development and reseach of various quantum magnetometers, have been reviewed briefly. In authors' opinion, the quantum magnetometers include nuclear precession magnetometer(proton, He3) , Overhauser - Abraham proton magnetometer , optical pumping magnetometer (He4、K、Cs、Rb), atomic magnetometer and superconducting magnetometer. This paper is involved with five kinds of magnetometers in more details. 1) Overhauser - Abraham proton magnetometers made in France and Canada , would be successor of common proton magnetometer. 2) New He4 optically pumping magnetometer, developed by french J. M. Leger et al, using laser with D0 line as light source (Dr. R. E. Slocm et al. in America have done similar work independently. ) , offers good sensitivity and accuracy. The deviation from omni -directionality is small. It is suggested to ESA to use this type of magnetometer for satellite magnetic measurement.3) Alkali vapour magnetometers(K、Cs、Rb), using lamps with D1 line as light source, are widely applied in geophysical survey. 4) The all -optical magnetometer developed by M. V. Romalis, J. C. Allred et al. , sensitivity of 0. 54fT/Hzl/2(lfT=10-15T) has already been a-chieved, and 10-18 T could be reached. 5) Superconducting magnetometer (SQUID) when working below 4°K, is the most sensitive, and the HTC SQUIDs , recently made in America and Europe, are portable, and can be used for magnetic and EM surveying.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".