In Situ control and modification of the probe magnetization state for accurate magnetic force microscopy
Bibliographic record
Abstract
Electrostatic tip-sample interactions currently represent the main limitation to accurate quantitative analysis of magnetic force microscopy (MFM) data. Controlled magnetization MFM (CM-MFM) represents a smart solution to overcome this limitation as it allows one to identify electrostatic artifacts and to subtract them from standard MFM images, thus enabling the quantitative investigation of magnetic properties of materials at the nanometer scale. CM-MFM, however, requires not only the magnetization, but also the in situ accurate demagnetization of the MFM probe. In particular, the latter represents a crucial step for the complete removal of electrostatic artifacts. In this work, we describe two different methods to depolarize the MFM tip, based on the application of the coercive remanent magnetic field of the tip and on a damped alternating magnetic field, respectively. The two techniques are escribed and compared to emphasize their specific advantages and limitations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".