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Record W4412795174 · doi:10.3791/68335

Non-aversive Animal Restraint Enabling Recording of Optomotor Reflex in Ground Squirrels

2025· article· en· W4412795174 on OpenAlexaff
Kiyoharu J. Miyagishima, Francisco M. Nadal‐Nicolás, J M Ball, Thomas A. Münch, Boris Benkner, Wei Li

Bibliographic record

VenueJournal of Visualized Experiments · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsOptech (Canada)
FundersNational Institutes of Health
KeywordsReflexNeuroscienceAversive StimulusBiologyMedicine

Abstract

fetched live from OpenAlex

The optomotor reflex (OMR) provides a behavioral assessment of an animal's contrast sensitivity and visual acuity. Mice or rats are typically placed directly onto a small circular platform by hand; however, handling animals like this can stimulate stress and anxiety, which introduce confounding factors when interpreting data. It has been shown that non-aversive handling methods, such as picking up mice or rats in a familiar tunnel/tube, can reduce anxiety. This is of particular interest in studies where animals display heightened stress, overactivity, or motor dysfunction, resulting in an inability to stay on the platform. A team led by Drs. Kiyoharu J. Miyagishima and Francisco M. Nadal-Nicolás have redesigned the conventional OMR platform to provide semi-closed containment. This makes it possible for the first time to record the optomotor reflex in the 13-lined ground squirrel, which is one of the few mammals that can see color. It has a visual streak with a high density of cones similar to the human macula providing an attractive model for studying effects on the cone visual system.

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: Bench or experimental · Consensus signal: Bench or experimental
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.438
Teacher spread0.404 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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