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Record W4380422976 · doi:10.7554/elife.84967

Flying squirrels use a mortise-tenon structure to fix nuts on understory twigs

2023· article· en· W4380422976 on OpenAlexaff
Han Xu, Xia Lian, John R. Spence, Mingxian Lin, Chunyang Lu, Yanpeng Li, Jie Chen, Tushou Luo, Yide Li, Suqin Fang

Bibliographic record

VenueeLife · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUnderstoryCanopyMortise and tenonRainforestBiologyHorticultureAgroforestryBotanyEcologyGeographyArchaeology

Abstract

fetched live from OpenAlex

Squirrels of temperate zones commonly store nuts or seeds under leaf litter, in hollow logs, or even in holes in the ground; however, in the humid rainforests of Jianfengling in Hainan, South China, we show that some flying squirrels cache elliptical or oblate nuts by hanging them securely in vegetation. These small flying squirrels were identified as Hylopetes phayrei electilis (G. M. Allen, 1925) and Hylopetes alboniger (Hodgson, 1870), in video clips captured of their behavior around focal nuts. Squirrels chewed grooves encircling ellipsoid nuts or distributed on the bottoms of oblate nuts, and then used these grooves to fix nuts tightly between small twigs 0.1–0.6 cm in diameter that were connected at angles of 25–40°. The grooves carved on the nuts (concave structure) connected with Y-shaped twigs (convex structure) and thus firmly affixed the nuts to the plant in a way similar to a mortise-tenon joint used in architecture and carpentry. Cache sites were on small plants located 10–25 m away from the closest potentially nut-producing tree, a behavior that likely reduces the discovery and consumption of the nuts by other animals. The adaptive squirrel behavior that shapes and fits nuts between twigs seems to be directed at providing more secure storage that increases food supply during dry periods in a humid tropical rainforest. In addition to providing such benefits for the squirrels, we suggest that this behavior also impacts the distribution of tree species in the forest.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.054
GPT teacher head0.290
Teacher spread0.236 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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