The propensity for re-triggered predation fear in a prey fish
Bibliographic record
Abstract
Variation in predation risk can drive variation in fear intensity, the length of fear retention, and whether fear returns after waning. Using Trinidadian guppies, we assessed whether a low-level predation threat could easily re-trigger fear after waning. First, we show that background risk induced neophobia after either multiple exposures to a low-level threat or a single exposure to a high-level threat. However, a single exposure to the low-level threat had no such effect. The individuals that received multiple background exposures to the low-level threat retained their neophobic phenotype over an 8-day post-risk period, and this response was intensified by a single re-exposure to the low-level threat on day 7. In contrast, the neophobia following the single high-level threat waned over the 8-day period, but the single re-exposure to the low-level threat on day 7 re-triggered the neophobic phenotype. Thus, despite the single low-level exposure being insufficient to induce neophobia, it significantly elevated existing fear and re-triggered fear that had waned. We highlight how such patterns of fear acquisition, retention, and rapid re-triggering play an important role in animal ecology and evolution and outline parallels between the neophobic phenotype in fishes and dimensions of post-traumatic stress in humans.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".