Water shrews detect movement, shape, and smell to find prey underwater
Bibliographic record
Abstract
American water shrews (Sorex palustris) are aggressive predators that feed on a variety of terrestrial and aquatic prey. They often forage at night, diving into streams and ponds in search of food. We investigated how shrews locate submerged prey using high-speed videography, infrared lighting, and stimuli designed to mimic prey. Shrews attacked brief water movements, indicating motion is an important cue used to detect active or escaping prey. They also bit, retrieved, and attempted to eat model fish made of silicone in preference to other silicone objects showing that tactile cues are important in the absence of movement. In addition, water shrews preferentially sniffed model prey fish and crickets underwater by exhaling and reinhaling air through the nostrils, suggesting olfaction plays an important role in aquatic foraging. The possibility of echolocation, sonar, or electroreception was investigated by testing for ultrasonic and audible calls above and below water and by presenting electric fields to foraging shrews. We found no evidence for these abilities. We conclude that water shrews detect motion, shape, and smell to find prey underwater. The short latency of attacks to water movements suggests shrews may use a flush-pursuit strategy to capture some 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".