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
Human illness attribution has been recently recognized as an important tool to better inform food safety decisions. Analysis of outbreak data sets has been used for that purpose. This study was conducted to explore the usefulness of three comprehensive Canadian foodborne outbreak data sets covering 30 years for estimating food attribution in cases of gastrointestinal illness, providing Canadian food attribution estimates from a historical perspective. Information concerning the microbiological etiology and food vehicles recorded for each outbreak was standardized between the data sets. The agent-food vehicle combinations were described and analyzed for changes over time by using multiple correspondence analysis. Overall, 6,908 foodborne outbreaks were available for three decades (1976 through 2005), but the agent and the food vehicle were identified in only 2,107 of these outbreaks. Differences between the data sets were found in the distribution of the cause, the vehicle, and the location or size of the outbreaks. Multiple correspondence analysis revealed an association between Clostridium botulinum and wild meat and between C. botulinum and seafood. This analysis also highlighted changes in food attribution over time and generated the most up-to-date food attribution values for salmonellosis (29% of cases associated with produce, 15% with poultry, and 15% with meat other than poultry, pork, and beef), campylobacteriosis (56% of cases associated with poultry and 22% with dairy products other than fluid milk), and Escherichia coli infection (37% of cases associated with beef, 23% with cooked multi-ingredient dishes, and 11% with meat other than beef, poultry, and pork). Because of the inherent limitations of this approach, only the main findings should be considered for policy making. The use of other human illness attribution approaches may provide further clarification.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.003 |
| 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 it