Bibliographic record
Abstract
If you spend more energy catching your next meal than it provides, you run the risk of creating an energy deficit, which will in turn negatively impact your growth and potentially your survival. This suggests that predators may adaptively choose prey that are more economically profitable. Klemen Koselj and colleagues, from the University of Tubingen and the Max Planck Institute for Ornithology in Germany, set out to determine whether prey selection in horseshoe bats is based on optimizing energy profitability. The team wanted to see whether the bats could learn which prey offered the greatest amount of energy, whether they could estimate how often they would encounter their prey, and whether they could integrate the two pieces of information and make prey selection decisions that would help to maximize their energy intake.First, the team designed two different sized rotating propellers that simulated two different sized prey items and created echo patterns that the bats could distinguish using echolocation. The team then trained horseshoe bats to associate the large propeller with a large mealworm reward, and the small propeller with a small mealworm reward. Following training, the team ran each bat through a series of hunting trials where they were sequentially and repeatedly offered both large and small propellers. The order in which the propellers were presented was varied for each trial. The team also varied the frequency with which they were presented to mimic the effect of different prey densities and abundances. In theory, if the bats were making economical decisions about their meals, the more abundant the large prey the less often the bats should respond to smaller prey.In fact, as the frequency of the larger prey increased (i.e. the more often the large propeller was presented to the bats), the bats preyed more predominantly on the larger prey and more often ignored smaller prey when it was presented. Conversely, as the frequency of the larger prey was decreased, the bats began to feed on both large and small prey items equally. This suggests that not only can bats distinguish prey items based on their energy content but also they can estimate their abundance based on how frequently they encounter the prey. These two pieces of information then contribute to the bats' prey choice, helping them to make adaptive decisions. This suggests that the prey selection biologists see in the field may be a result of bats choosing the most profitable prey.Koselj and colleagues have shown that bats can and do make economical decisions when hunting. Having a surplus in your energy budget allows for growth and reproduction, and choosing prey that gives you the biggest bang for your buck would certainly help you achieve that. Overall, this suggests that predators may be making complicated decisions about prey selection and that foraging may be based on more than happenstance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".