Superlattice correlations in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>Tb</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mrow><mml:mtext>Ti</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mtext>O</mml:mtext><mml:mn>7</mml:mn></mml:msub></mml:mrow></mml:math>under the application of [110] magnetic field
Bibliographic record
Abstract
We report an analysis of neutron diffraction from single crystals of the spin-liquid pyrochlore ${\text{Tb}}_{2}{\text{Ti}}_{2}{\text{O}}_{7}$ under the application of magnetic fields along the crystallographic [110] direction. Such a perturbation has been shown to destroy the spin-liquid ground state and induce long-range order, although the nature of the ordered state was not immediately determined. Recently, it has been proposed that the ordered state is characterized by spin-ice-like correlations, evincing an emergent ferromagnetic tendency in this material despite the large negative Curie-Weiss constant. Here, we argue instead that the ordered state is dominated by $\mathbit{Q}\ensuremath{\ne}0$ correlations that emerge either from strong antiferromagnetism or magnetoelastic distortion of the crystal. In contrast to previous reports, we observe no evidence for re-entrant behavior in the high-field limit. Extreme sensitivity of the ordered state to the alignment of the applied field is suggested to account for these discrepancies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.119 | 0.008 |
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".