Correction: Specialized Learning in Antlions (Neuroptera: Myrmeleontidae), Pit-Digging Predators, Shortens Vulnerable Larval Stage
Bibliographic record
Abstract
Unique in the insect world for their extremely sedentary predatory behavior, pit-dwelling larval antlions dig pits, and then sit at the bottom and wait, sometimes for months, for prey to fall inside.This sedentary predation strategy, combined with their seemingly innate ability to detect approaching prey, make antlions unlikely candidates for learning.That is, although scientists have demonstrated that many species of insects possess the capacity to learn, each of these species, which together represent multiple families from every major insect order, utilizes this ability as a means of navigating the environment, using learned cues to guide an active search for food and hosts, or to avoid noxious events.Nonetheless, we demonstrate not only that sedentary antlions can learn, but also, more importantly, that learning provides an important fitness benefit, namely decreasing the time to pupate, a benefit not yet demonstrated in any other species.Compared to a control group in which an environmental cue was presented randomly vis-a `-vis daily prey arrival, antlions given the opportunity to associate the cue with prey were able to make more efficient use of prey and pupate significantly sooner, thus shortening their long, highly vulnerable larval stage.Whereas ''median survival time,'' the point at which half of the animals in each group had pupated, was 46 days for antlions receiving the Learning treatment, that point never was reached in antlions receiving the Random treatment, even by the end of the experiment on Day 70.In addition, we demonstrate a novel manifestation of antlions' learned response to cues predicting prey arrival, behavior that does not match the typical ''learning curve'' but which is well-adapted to their sedentary predation strategy.Finally, we suggest that what has long appeared to be instinctive predatory behavior is likely to be highly modified and shaped by learning.
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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 | 0.002 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.145 | 0.053 |
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".