Technical note: The impact of image size on bloodstain pattern analysis using machine learning
Bibliographic record
Abstract
Classification of bloodstain patterns can occur at the scene or through (digital) images submitted for analysis and/or peer review. Capturing high-resolution images requires high-quality cameras or scanners and can be time consuming, especially when image stitching is needed. With the increasing use of machine learning (ML) for image analysis, handling large images is also computationally demanding. This raises the question of whether high-resolution images are necessary for accurate ML based pattern classification. In this study, we explore the role of image size on bloodstain classification by replicating an existing experiment distinguishing impact versus forward spatter, using both original and resized images. Despite expectations, reducing resolution did not significantly affect classification accuracy. This suggests that modern ML techniques may be less reliant on fine image detail than human analysts. We also examined the importance of feature selection in classification. Using only four key features showed results comparable to those obtained using all 58 from the original model. This points to the potential for simpler, more efficient models without sacrificing accuracy. These results raise important questions, while the model performed well under reduced input conditions, it is essential to understand what it relies on and why. Rather than applying ML models blindly, we emphasize the need to understand their behavior, for responsible and effective use in forensic workflows.
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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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".