Bibliographic record
Abstract
For millions of years, parasites have altered the behaviour of their hosts. Parasites can affect host behaviour by: (1) interfering with the host's normal immune-neural communication, (2) secreting substances that directly alter neuronal activity via non-genomic mechanisms and (3) inducing genomic- and/or proteomic-based changes in the brain of the host. Changes in host behaviour are often restricted to particular behaviours, with many other behaviours remaining unaffected. Neuroscientists can produce this degree of selectivity by targeting specific brain areas. Parasites, however, do not selectively attack discrete brain areas. Parasites typically induce a variety of effects in several parts of the brain. Parasitic manipulation of host behaviour evolved within the context of the manipulation of other host physiological systems (especially the immune system) that was required for a parasite's survival. This starting point, coupled with the fortuitous nature of evolutionary innovation and evolutionary pressures to minimize the costs of parasitic manipulation, likely contributed to the complex and indirect nature of the mechanisms involved in host behavioural control. Because parasites and neuroscientists use different tactics to control behaviour, studying the methods used by parasites can provide novel insights into how nervous systems generate and regulate behaviour. Studying how parasites influence host behaviour will also help us integrate genomic, proteomic and neurophysiological perspectives on behaviour.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".