Use of stable isotopes to reveal trophic relationships and transmission of a food-borne pathogen
Bibliographic record
Abstract
Predators in food webs are valuable sentinel species for zoonotic and multi-host pathogens such as Toxoplasma gondii. This protozoan parasite is ubiquitous in warm-blooded vertebrates, and can have serious adverse effects in immunocompromised hosts and foetuses. In northern ecosystems, T. gondii is disproportionately prevalent in Inuit people and wildlife, in part due to multiple routes of transmission. We combined data on T. gondii infection in foxes from Nunavik (northern Québec, Canada) with stable isotope data tracking trophic relationships between foxes and several of their main prey species. Red (Vulpes vulpes) and Arctic fox (Vulpes lagopus) carcasses were collected by local trappers from 2015 to 2019. We used magnetic capture PCR to detect DNA of T. gondii in heart and brain tissues, and enzyme-linked immunosorbent assay to detect antibodies in blood. By linking infection status with diet composition, we showed that infected foxes had a higher probability of consuming aquatic prey and migratory geese, suggesting that these may be important sources of T. gondii transmission in the Arctic. This use of stable isotopes to reveal parasite transmission pathways can be applied more broadly to other foodborne pathogens, and provides evidence to assess and mitigate potential human and animal health risks associated with T. gondii in northern ecosystems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".