Temperature-dependent optical spectroscopy studies of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="normal">Nd</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mi mathvariant="normal">Ti</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>
Bibliographic record
Abstract
The temperature dependent optical spectra are investigated in a well-characterized titanate system, ${\mathrm{Nd}}_{1\ensuremath{-}x}\mathrm{Ti}{\mathrm{O}}_{3}$, between 50 and $40 000\phantom{\rule{0.3em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$ at three different doping levels, $x=0.019$, 0.046, and 0.095, corresponding to a Mott-Hubbard insulator, a semiconductor and a correlated metal, respectively. Mid-gap states develop inside the Hubbard gap with hole doping. Based on the room-temperature spectra of the optical conductivity, the evolution rate of the excitations below $1.2\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ with doping is dependent on the electron correlation strength $(U∕W)$ of the parent insulator, which has been observed in other titanates as well. In the metallic sample $(x=0.095)$, an anomalous enhancement of spectral weight below $1\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ develops with decreasing temperature. The partial spectral weight shows a quadratic dependence on temperatures up to the plasma frequency. Meanwhile, the metallic sample displays a Fermi-liquid behavior at low frequencies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".