Helminth-host-environment interactions: Looking down from the tip of the iceberg
Bibliographic record
Abstract
In 1978, the theory behind helminth parasites having the potential to regulate the abundance of their host populations was formalized based on the understanding that those helminth macroparasites that reduce survival or fecundity of the infected host population would be among the forces limiting unregulated host population growth. Now, 45 years later, a phenomenal breadth of factors that directly or indirectly affect the host-helminth interaction has emerged. Based largely on publications from the past 5 years, this review explores the host-helminth interaction from three lenses: the perspective of the helminth, the host, and the environment. What biotic and abiotic as well as social and intrinsic host factors affect helminths? What are the negative, and positive, implications for host populations and communities? What are the larger-scale implications of the host-helminth dynamic on the environment, and what evidence do we have that human-induced environmental change will modify this dynamic? The overwhelming message is that context is everything. Our understanding of second-, third-, and fourth-level interactions is extremely limited, and we are far from drawing generalizations about the myriad of microbe-helminth-host interactions.Yet the intricate, co-evolved balance and complexity of these interactions may provide a level of resilience in the face of global environmental change. Hopefully, this albeit limited compilation of recent research will spark new interdisciplinary studies, and application of the One Health approach to all helminth systems will generate new and testable conceptual frameworks that encompass our understanding of the host-helminth-environment triad.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".