Taking cues from ecological and evolutionary theories to expand the landscape of disgust
Bibliographic record
Abstract
Behavioural avoidance of parasites in the environment generates what is known as the 'landscape of disgust' (analogous to the predator-induced 'landscape of fear'). Despite the potential for improving our inference of host-parasite dynamics, three limitations of the landscape of disgust restrict the insight that is gained from current research: (i) many host-parasite systems will not be appropriate for invoking the landscape of disgust framework; (ii) existing research has primarily focused on immediate choices made by hosts on small scales, limiting predictive power, generalizability, and the value of the insight obtained; and (iii) relevant ecological and evolutionary theory has yet to be integrated into the framework, challenging our ability to interpret the landscape of disgust within the context of most host-parasite systems. In this review, we explore the specific requirements for implementing a landscape of disgust framework in empirical systems. We also propose greater integration of habitat selection and evolutionary theories, aiming to generate novel insight, by exploring how the landscape of disgust varies within and across generations, presenting opportunities for future research. Despite interest in the impacts of parasitism on animal movement and behaviour, many unanswered questions remain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".