Modeling the mid-infrared optical gap in La2−xSrxCuO4
Bibliographic record
Abstract
In this work, we used a periodic lattice potential in order to model the infrared optical data of the high-temperature superconductor La2−xSrxCuO4. This potential consists of a two-dimensional array of double-well potentials, which simulate the CuO2 layers. It is obtained by assembling Cu-O-Cu units rather than Cu and O single atoms in the tight-binding approach. A gap separating two energy bands can be obtained and is used to fit the infrared (IR) optical gap of this cuprate. We derived the dielectric function and showed that in the classical limit it reduces to the one consisting of a Drude term plus a number of lorentz components, equivalent to the dielectric function used empirically by several authors in their fits of the reflectivity. By refitting available reflectance data, we deduced a simple law for the doping dependence of the optical gap in La2−xSrxCuO4. In the present study, we argue that the optical gap is distinct from the pseudogap or the two-magnon gap, because it characterizes La2−xSrxCuO4 for all doping regimes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".