Quantifying Diarrheal Characteristics: A Computer Vision Approach for Global Health
Bibliographic record
Abstract
Diarrheal diseases have been responsible for millions of deaths annually and need to be combatted with better health surveillance. Advances in diarrhea surveillance are obstructed by the lack of knowledge about how diarrhea exits the body. Therefore, a method was created to use calibrated optical flow velocimetry data to measure diarrheal properties from videos. The results show diarrhea’s erratic behaviour, including spray angles, velocity progression, and widths. Velocities of diarrhea averaged 1.31 m/s with an observed maximum of around 8 m/s. Spray widths typically ranged 2 – 5 cm. The angle that diarrhea exits the body varied from the vertical by a standard deviation of 7 degrees. The created and deployed method of quantifying diarrhea has already advanced our knowledge about diarrhea and has the potential to enable better global health surveillance and ultimately reduce the diarrheal disease burden.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".