Food and environmental parasitology in Canada: A network for the facilitation of collaborative research
Bibliographic record
Abstract
Parasitic diseases are of considerable public health significance in Canada, particularly in rural and remote areas. Food- and water-borne parasites contribute significantly to the overall number of parasitic infections reported in Canada. While data on the incidence of some of these diseases are available, knowledge of the true burden of infection by the causative agents in Canadians is somewhat limited. A number of centers of expertise in Canada study various aspects of parasitology, but few formal societies or networks of parasitologists currently exist in Canada, and previously none focused specifically on food or environmental transmission. The recently established Food and Environmental Parasitology Network (FEPN) brings together Canadian researchers, regulators and public health officials with an active involvement in issues related to these increasingly important fields. The major objectives of the Network include identifying research gaps, facilitating discussion and collaborative research, developing standardized methods, generating data for risk assessments, policies, and guidelines, and providing expert advice and testing in support of outbreak investigations and surveillance studies. Issues considered by the FEPN include contaminated foods and infected food animals, potable and non-potable water, Northern and Aboriginal issues, zoonotic transmission, and epidemiology.
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.
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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".