Accurate representation of interference colours (Michel–Lévy chart): from rendering to image colour correction
Bibliographic record
Abstract
Summary Here a work flow towards an accurate representation of interference colours (Michel‐Lévy chart) digitally captured on a polarised light microscope using dry and oil immersion objectives is presented. The work flow includes accurate rendering of interference colours considering the colour temperature of the light source of the microscope and chromatic adaptation to white points of RGB colour spaces as well as the colour correction of the camera using readily available colour targets. The quality of different colour correction profiles was tested independently on an IT8.7/1 target. The best performing profile was using the XYZ cLUT algorithm and it revealed a ΔE00 of 1.9 (6.4 no profile) at 5× and 1.1 (8.4 no profile) at 100× magnification, respectively. The overall performance of the workflow was tested by comparing rendered interference colours with colour‐corrected images of a quartz wedge captured over a retardation range from 80–2500 nm at 5× magnification. Uncorrected images of the quartz wedge in sRGB colour space revealed a mean ΔE00 of 12.3, which could be reduced to a mean of 4.9 by applying a camera correction profile based on an IT8.7/1 target and the Matrix only algorithm (ΔE00 < 1.0 signifies colour differences imperceptible by the human eye). ΔE00 varied significantly over the retardation range of 80–2500 nm of the quartz wedge, but the reasons for this variation is not well understood and the quality of colour correction might be further improved in future by using custom made colour targets specifically designed for the analysis of high‐order interference colours.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".