Using <i>k</i>-means to sort spectra: Electronic order mapping from scanning tunneling spectroscopy measurements
Bibliographic record
Abstract
Hyperspectral imaging techniques have a unique ability to probe the inhomogeneity of material properties whether driven by compositional variation or other forms of phase segregation. In the doped cuprates, iridates, and related materials, scanning tunneling microscopy/spectroscopy (STM/STS) measurements have found the emergence of pseudogap “puddles” from the macroscopically Mott insulating phase with increased doping. However, categorizing this hyperspectral data by electronic order is not trivial and has often been done with ad hoc methods. In this paper, we demonstrate the utility of k-means, a simple and easy-to-use unsupervised clustering method, as a tool for classifying heterogeneous scanning tunneling spectroscopy data by electronic order for Rh-doped Sr2IrO4, a cuprate-like material. Applied to STM data acquired within the Mott phase, k-means was able to identify areas of Mott order and of pseudogap order. The unsupervised nature of k-means limits avenues for bias and provides clustered spectral shapes without a priori knowledge of the physics. Additionally, we demonstrate the use of k-means as a preprocessing tool to constrain phenomenological function fitting. Clustering the data allows us to reduce the fitting parameter space, limiting over-fitting. We suggest k-means as a fast, simple model for processing hyperspectral data on materials of mixed electronic order.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".