Living with liver flukes: Does migration matter?
Bibliographic record
Abstract
) in faeces across migratory tactics. We tested whether giant liver fluke intensity in faeces was affected by elk use of wetlands, elevation, forage biomass, and elk concentration in the previous summer. We rejected the "migratory escape" hypothesis that suggests migration allowed elk to escape parasite exposure because migrant elk had the highest richness and evenness of parasite groups. We also rejected the hypothesis that prevalence was highest at highest summer densities because higher-density resident elk had the lowest diversity and giant liver fluke egg presence and intensity. Instead, the high prevalence and intensity of giant liver flukes in migrants was consistent with both the hypothesis of "environmental tracking", because elk that migrated earlier may expose themselves to favourable parasite conditions, and with the "environmental sampling" hypothesis, because giant liver fluke intensity increased with increased exposure to secondary host habitat (i.e., wetland). Our results indicate that differential exposure of different migratory tactics that leave the winter range has a greater influence on parasites than the concentration of elk that reside on the winter range year-round.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".