Magnetic properties of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>In</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>containing<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Fe</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math>nanoparticles
Bibliographic record
Abstract
Films of Fe-doped ${\mathrm{In}}_{2}{\mathrm{O}}_{3}$ that were deliberately fabricated so they contained ${\mathrm{Fe}}_{3}{\mathrm{O}}_{4}$ nanoparticles were deposited on sapphire substrates by pulsed laser deposition at low oxygen pressure. The concentration of Fe was varied between 1% and 5%, and the effect of including 5% of Sn and vacuum annealing were also investigated. Structural analysis indicated a high concentration of ${\mathrm{Fe}}_{3}{\mathrm{O}}_{4}$ nanoparticles that caused substantial values of the coercive field at room temperature. Transport measurements indicated that the films were metallic, and an anomalous Hall effect was observed for the sample with 5% of Fe. The concentration of nanoparticles was reduced dramatically by the inclusion of 5% of Sn. Magnetic circular dichroism spectra taken in field and at remanence were analyzed to show that the samples had a magnetically polarized defect band located below the conduction band as well as magnetic ${\mathrm{Fe}}_{3}{\mathrm{O}}_{4}$ nanoparticles. The signal from the defect states near the band edge was enhanced by increasing the number of carriers by either including Sn or by annealing in vacuum.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".