<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Fe</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mi mathvariant="normal">Ni</mml:mi></mml:math>-type chemical order in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Fe</mml:mi></mml:mrow><mml:mrow><mml:mn>65</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Ni</mml:mi></mml:mrow><mml:mrow><mml:mn>35</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>films grown by evaporation: Implications regarding the Invar problem
Bibliographic record
Abstract
By use of Monte Carlo simulations of chemical ordering, magnetic ordering, and magnetovolume thermal effects and by reviewing the known effects of chemical order in the face-centered-cubic Fe-Ni alloy system, we show that the observations of Dumpich et al. [Phys. Rev. B 46, 9258 (1992)], who report unique Invar-composition ${\mathrm{Fe}}_{65}{\mathrm{Ni}}_{35}$ samples that exhibit Invar behavior despite being collinear ferromagnets with no deviation from the Slater-Pauling curve, are consistent with the effects of varying degrees of ${\mathrm{Fe}}_{3}\mathrm{Ni}$-type chemical order, which in turn are consistent with the sample preparation and treatment methods used. This allows us to make certain conclusive statements concerning models for Invar behavior and the nature of ${\mathrm{Fe}}_{3}\mathrm{Ni}.$
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.522 | 0.611 |
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".