Are fast explorers slow reactors? Linking personality type and anti-predator behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Response delays to predator attack may be adaptive, suggesting that latency to respond does not always reflect predator detection time, but can be a decision based on starvation-predation risk trade-offs. In birds, some anti-predator behaviours have been shown to be correlated with personality traits such as activity level and exploration. Here, we tested for a correlation between exploration behaviour and response latency time to a simulated fish predator attack in a fish species, juvenile convict cichlids (Amatitlania nigrofasciata). Individual focal fish were subjected to a standardized attack by a robotic fish predator while foraging, and separately given two repeated trials of exploration of a novel environment. We found a strong positive correlation between exploration and time taken to respond to the predator model. Fish that were fast to explore the novel environment were slower to respond to the predator. Our study therefore provides some of the first experimental evidence for a link between exploration behaviour and predator-escape behaviour. We suggest that different behavioural types may differ in how they partition their attention between foraging and anti-predator vigilance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it