Kernel-inspired algorithm to transform transmission electron microscopy images into discrete dipole approximation geometries
Bibliographic record
Abstract
In this work, we present a code that transforms 2D transmission electron microscopy images into 3D geometries for discrete dipole approximation simulations in DDSCAT 7.3.3 based on Python 3.11 and OpenCV 4.8.1. This allows for the extrapolation of experimental sample images into ready-to-use simulation geometries. The advantage is that the geometry reflects complex shapes instead of approximations of basic shapes like spheres, cylinders, or cubes. The underlying algorithm to extrapolate 2D images to 3D structures is inspired by the working principle of kernels used in image processing. To showcase the code, the absorption spectrum of deposited gold nanoparticles was simulated and compared with experimental values. Apart from a small systematic shift of the simulated spectrum, it is in excellent agreement with the experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".