MétaCan
Menu
Back to cohort
Record W4318619737 · doi:10.1242/jeb.244990

Great gray owls overcome sound illusion to hunt

2023· article· en· W4318619737 on OpenAlexaboutno aff
Jonaz Moreno Jaramillo

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsSnowLoudspeakerSound (geography)PredationMicrotusDiggingGeographyEcologyGeologyArchaeologyAcousticsMeteorologyOceanographyBiology

Abstract

fetched live from OpenAlex

Have you ever heard how quiet owls are when they fly? They are very stealthy, giving them a distinct advantage over their prey. Owls can also pick up sounds that are almost inaudible to the human ear while hunting. However, great gray owls (Strix nebulosa) live in cold environments, often with snow on the ground, which can make hunting by ear more difficult, because their prey, such as voles (Cricetidae), hide beneath the snow and the sounds they produce could be dampened by the icy covering. Christopher Clark from University of California, Riverside, USA, James Duncan from Discover Owls, Canada, and Robert Dougherty from University of Washington, USA, wanted to know how the sounds produced by prey are affected by the presence of snow and how the owl overcomes these acoustic challenges to hunt.First, the team wanted to find out how snow affects how sound carries. They went out into the field during the winter (February) in Manitoba, Canada, to identify locations where the owls had been hunting, and found seven holes in the snow produced by owls as they retrieved their food. Then, they dug 40 cm deep holes near the owls’ hunting sites and placed a waterproof loudspeaker at the bottom; they also placed an acoustic camera 1–1.5 m above the snowpack and 1.2–6 m from the loudspeaker. The team then played a recording of the sounds produced by a meadow vole (Microtus pennsylvanicus) digging beneath the snow through the loudspeaker, while gradually scraping the snow away in layers, recording the volume and location of the sound relative to the speaker at six snow depths. They used this information to simulate how the owl would perceive the sound after it traveled through the snow.They found that snow does in fact act as a muffler for sound produced by rodents buried beneath it, especially for high-pitched sounds, such as when the voles are communicating with one another. As the snow was removed, the sound level increased and the location of the sound also appeared to move, with the sound source appearing to be displaced farthest to one side of the speaker when the snow was deepest, moving closer to the speaker as the snow was removed until it appeared to come directly from the speaker when all the snow was gone. The team suggests that owls could overcome this challenge by positioning themselves well above the snow, either on a perch or flying high, to reduce the likelihood of being misled by the distorted sound position. And it seems that the great gray owls have already come to the same conclusion as they often hover directly above their prey before plunging into the snow, to increase their accuracy.The work done by Clark and colleagues highlights how the snow creates a sound illusion by bending the path of the sound – much like light is bent when passing through a glass of water, making a straw appear bent – directly affecting how great gray owls target food beneath snow cover. The birds have also evolved to fly extremely silently, diminishing the noise produced by their own flight, allowing them to overcome this sound illusion and hear voles digging beneath the snow while they hover above. One could say that these birds are the ninjas of the sky.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.041
GPT teacher head0.363
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicAnimal Vocal Communication and BehaviorFrench-language works237,207