Multiscale ecological drivers of Echinococcus multilocularis spatial distribution in wild hosts: A systematic review
Bibliographic record
Abstract
Understanding the ecological factors that drive the spatial patterns of parasites transmission is essential to predict their distribution under global change and to direct proactive surveillance efforts. Here, we systematically reviewed the literature to assess the main ecological drivers responsible for the spatial distribution and transmission of the zoonotic cestode Echinococcus multilocularis, the aetiological agent of alveolar echinococcosis, focusing on wild hosts. The 23 retrieved studies suggested that the dispersal of definitive hosts, climatic and biotic factors (distribution of intermediate hosts, composition of host communities) shape continental-scale distribution patterns of E. multilocularis, whereas the relative importance of climate and land cover in driving E. multilocularis distribution at a smaller (country/regional) scale varies with the geographic area considered. At a local scale, two additional factors contribute to determine the distribution of micro-foci of transmission: the trophic relationships between carnivores definitive hosts and small mammals intermediate hosts, and the defecation and marking behaviour of definitive hosts.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".