Combining Experimental and Theoretical Evidence to Understand Predator Learning Behaviour with Unfamiliar Prey
Bibliographic record
Abstract
Predators confronted with an unfamiliar prey must decide whether or not to attack it.This decision is dependent on the predator's previous experiences with the prey type.In this thesis, I begin by testing the ability of Jamaican field crickets, Gryllus assimilis, to learn a novel binary food choice between a rewarding and unrewarding prey using visual discriminatory cues.In this experiment, evidence of learning was confirmed across trials.Moreover, the colour of the prey item significantly affected the probability of crickets choosing the palatable option, with palatable green prey more likely to be attacked than palatable blue prey.I then developed a model that formalized prey selection in terms of an exploration-exploitation trade-off.With this model, I identified the optimal sampling strategy for a predator with Bayesian learning.I demonstrate that a predator's prior beliefs (Bayesian priors), and the certainty it has in its beliefs (variance in prior) affect the optimal sampling strategy, and hence the nature of selection it places on prey.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".