Bibliographic record
Abstract
Abstract The magnetic properties of ultrathin ferromagnetic films a few monolayers thick differ from the magnetic properties of the same material in bulk form because a large fraction of the atoms in such films are located either on the surfaces or within one monolayer of the surfaces. As a result, the magnetic properties of ultrathin films are very sensitive to surface roughness and to the proximity of foreign atoms at the interfaces with a substrate or with an overlayers film. The first part of this chapter provides an overview of three main methods for the preparation of ultrathin films: thermal deposition, laser pulse deposition, and magnetron sputtering. Emphasis is placed on techniques used to prepare high‐quality films having smooth surfaces and important examples are discussed in detail. The second part reviews magnetic anisotropies in ultrathin films: measuring techniques, the theory of magnetic anisotropies, and the effect of surface roughness on thin‐film anisotropies. The chapter closes with a guide to the literature on thin‐film magnetic anisotropy data for several interesting systems: Fe, Co, Ni films grown on various substrates; Fe films grown on GaAs crystals; Fe/Pt alloys grown on MgO; and bcc Ni films grown on GaAs(001).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".