Vector Overhauser magnetometer POS-4: experience and prospects of application
Bibliographic record
Abstract
The results of practical use of a POS-4 vector magnetometer, developed by the Research Laboratory of Quantum Magnetometry, UrFU (Yekaterinburg) and based on POS Overhauser sensors, are presented. Continuous measurements by POS-4 have been carried out at the Paratunka observatory (IKIR FEB RAS, Kamchatka) since 2015, were done at the Saint Petersburg observatory (GC RAS / IZMIRAN SPb Branch, Leningrad Region) in 2017-2018 and have been performed at the Arti observatory (Institute of Geophysics, UB RAS, Sverdlovsk Region) since 2020. On the new high-latitude observatory White Sea (IAGA code WSE, GC RAS / MSU, Nikolai Pertsov White Sea Biological Station , Karelia), POS-4 is used as a main variometer for magnetic measurements. In April 2019, the magnetometer was successfully used for field measurements on ice during the TRANSARCTIC expedition in the Barents Sea (AARI, Roshydromet). At the beginning of 2021 IZMIRAN started testing two POS-4 magnetometers at the Moskow observatory. According to the results of field and observatory measurements it was possible to identify the advantages and disadvantages of the magnetometer and provide the information for its developers for further modernization in order to improve its efficiency and reliability. Many years of experience in POS-4 application determine the areas where its scientific and applied usage will provide important results, for example, for magnetic measurements in the Arctic regions or for monitoring of active zones around volcanoes.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".