A step-by-step method to quantify coloration with digital photography
Bibliographic record
Abstract
Coloration is often used in biological studies, for example when studying social signaling or antipredator defense. Yet, few detailed and standardized methods are available to measure coloration using digital photography. Here we provide a step-by-step guide to help researchers quantify coloration from digital images. We first identify the do's and don'ts of taking pictures for coloration analysis. We then describe how to i) extract reflectance values with the software ImageJ; ii) fit and apply linearization equations to reflectance values; iii) scale and select the areas of interest in ImageJ; iv) standardize pictures; and v) binarize and measure the proportion of different colors in an area of interest. We apply our methodological protocol to digital pictures of painted turtles ( Chrysemys picta ), but the approach could be easily adapted to any species. More specifically, we wished to calculate the proportion of red and yellow on the neck and head of turtles. With this protocol, our main aims are to make coloration analyses with digital photography: • More accessible to researchers without a background in photography. • More consistent between studies.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.019 |
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".