Reconstruction of the Sizes of Spherical Particles from Their Shadow Images. Part I: Theoretical Considerations
Bibliographic record
Abstract
Abstract Imaging optical array probes (OAPs) have become conventional instruments in studies of cloud microphysics. Previous works have shown that the error particle sizing in OAPs may reach 100%. Correcting the particle size measurements is not a trivial task, since the error depends on its size and distance from the object plane. A new technique for the size reconstruction of spherical particles from its measured image is introduced here. This technique also enables the retrieval of the particle position along the depth of field in the sample volume. The essence of the algorithm consists in the deduction of size and position from the relationships between the size of the Poisson spots and the geometrical dimensions of the image. The retrieval technique has been tested on the simulated discrete binary diffraction images of spherical particles, similar to those produced by OAPs. The images were modeled using the Fresnel diffraction approximation. It is demonstrated that the new algorithm can be applied to discrete binary images of spherical particles consisting of more than three pixels in size. An important feature of the retrieval technique is that it does not depend on the pixel resolution, and it can be applied for any type of OAPs that use a monochromatic coherent source of illumination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".