Food and environmental parasitology in Canada: A network for the facilitation of collaborative research
Bibliographic record
Abstract
Parasitic diseases are of considerable public health \nsignificance in Canada, particularly in rural and remote \nareas. Food- and waterborne parasites contribute \nsignificantly to the overall number of parasitic infections \nreported in Canada. While data on the incidence of some \nof these diseases are available, knowledge of the true \nburden of infection by the causative agents in Canadians \nis somewhat limited. A number of centers of expertise \nin Canada study various aspects of parasitology, but few \nformal societies or networks of parasitologists currently \nexist in Canada, and previously none focused specifically \non food or environmental transmission. The recently \nestablished Food and Environmental Parasitology Network \n(FEPN) brings together Canadian researchers, regulators \nand public health officials with an active involvement in \nissues related to these increasingly important fields. \nThe major objectives of the Network include identifying \nresearch gaps, facilitating discussion and collaborative \nresearch, developing standardized methods, generating \ndata for risk assessments, policies, and guidelines, and \nproviding expert advice and testing in support of outbreak \ninvestigations and surveillance studies. Issues considered \nby the FEPN include contaminated foods and infected food \nanimals, potable and non-potable water, Northern and \nAboriginal issues, zoonotic transmission, and epidemiology
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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 | 0.000 | 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 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".