Dude, Everyone Wants Pattern Analysis Tools (DEWPAT): Tools for measuring visual pattern diversity from digital images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Exploring the diversity and function of complex colour patterns is a fundamental interest in ecology and evolutionary biology, but progress on many questions is limited by our ability to quantify diverse visual patterns. We address this problem by introducing Dude, Everyone Wants Colour Pattern Analysis Tools ( DEWPAT ), a Python package for characterizing multidimensional pattern complexity. DEWPAT is a flexible framework designed to extract a diversity of components of visual pattern complexity from standard RGB and multispectral images, including entropy (information content), average gradient magnitude (edge content), high frequency content (detail granularity), generalized variance (heterogeneity), and patch dissimilarity. DEWPAT offers optional image transformation functionality, including blurring to model receiver acuity and segmentation to reduce noise. Functions in this package return both quantitative measurements and graphical representations of color and pattern diversity. We demonstrate DEWPAT ’s key functions and applications with three empirical examples (longhorn beetles, anole lizards, and flowers). DEWPAT has the potential to quantitatively characterize previously intractable pattern phenotypes in ways that make their features available for biological analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it