THE OPTIMAL SAMPLING STRATEGY FOR UNFAMILIAR PREY
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
Precisely how predators solve the problem of sampling unfamiliar prey types is central to our understanding of the evolution of a variety of antipredator defenses, ranging from Müllerian mimicry to polymorphism. When predators encounter a novel prey item then they must decide whether to take a risk and attack it, thereby gaining a potential meal and valuable information, or avoid such prey altogether. Moreover, if predators initially attack the unfamiliar prey, then at some point(s) they should decide to cease sampling if evidence mounts that the type is on average unprofitable to attack. Here, I cast this problem as a "two-armed bandit," the standard metaphor for exploration-exploitation trade-offs. I assume that as predators encounter and attack unfamiliar prey they use Bayesian inference to update both their beliefs as to the likelihood that individuals of this type are chemically defended, and the probability of seeing the prey type in the future. I concurrently use dynamic programming to identify the critical informational states at which predator should cease sampling. The model explains why predators sample more unprofitable prey before complete rejection when the prey type is common and explains why predators exhibit neophobia when the unfamiliar prey type is perceived to be rare.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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