Pulsenet: A Program to Detect and Track Food Contamination Events
Bibliographic record
Abstract
Abstract PulseNet USA is a national foodborne surveillance program that facilitates standardized subtyping of foodborne bacteria through a network of over 70 public health laboratories throughout the United States. With over a decade of successes in the rapid detection, communication, and response to foodborne outbreaks, PulseNet has proven instrumental to the identification and investigation of scores of outbreaks associated with unintentional foodborne contamination. In an increasingly globalized economy, the importance of food safety and biosecurity has become increasingly paramount. An act of bioterrorism involving food products is likely to be recognized, at least initially, in the same manner as naturally occurring foodborne outbreaks, and response capabilities will rely heavily upon the capacity and efficiency of existing public health infrastructure. Preparedness efforts will require the extension and enhancement of foodborne surveillance programs beyond the national level, and increased reliance on multilateral cooperation and information sharing with international partners. The recent globalization of PulseNet, with the establishment of PulseNet International, has greatly expanded the reach of foodborne disease surveillance to include regional networks in Asia, Europe, Canada, Latin America, and the Middle East, ushering in a promising new era for improved food safety and public health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.023 |
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".