Dude, everyone wants pattern analysis tools (DEWPAT): Tools for measuring visual pattern complexity from digital images
Bibliographic record
Abstract
Abstract Understanding 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 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 information entropy, edge content (average gradient magnitude), high frequency content (detail granularity), heterogeneity and patch dissimilarity. DEWPAT offers 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 colour 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 complex pattern phenotypes in ways that make their features available for biological analysis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".