An optimization approach for biosurveillance in wastewater networks
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
Biosurveillance is the ongoing systematic collection, analysis, and interpretation of biospheric data essential to planning, implementing, and evaluating national security interests. Biosurveillance efforts often focus on disease activity and threats to human, animal, or plant health to improve situational awareness of and early warnings of disease emergence or activity. Wastewater-based epidemiology is an important subdomain of biosurveillance that monitors the consumption of, or exposure to, chemicals and pathogens at the community/population level. However, ecological, environmental, economic, cultural, and political variability yields an asymmetric landscape of threats, subsequent exposure(s), and community burden. This work presents an exact approach based on mixed-integer programming to optimize sensor placement in a wastewater network. The primary goal is to maximize the amount of information captured from the network while narrowing down the potential geographical sources of chemical or biological markers (i.e., signals). We use the wastewater systems of Los Angeles (USA) and Regina (Canada) to demonstrate the scalability of our approach in realistic scenarios and how it can enhance stakeholders’ ability to identify emerging threats or assess the effectiveness of strategic interventions post-deployment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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