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Record W3010865670 · doi:10.21769/bioprotoc.3564

Shared Pheromonal Communication of Specific Fear Between Adult Sprague Dawley Rats

2020· article· en· W3010865670 on OpenAlexaff
Samantha J. Carew, Abhinaba Ghosh

Bibliographic record

VenueBIO-PROTOCOL · 2020
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSex pheromonePheromoneOdorPsychologyNeuroscienceCommunicationAnimal communicationBiologyZoologyEcology

Abstract

fetched live from OpenAlex

Rats are highly social animals, and mainly communicate with one another in two ways: through ultrasonic vocalizations and pheromones. Most research on pheromones has been dedicated those regarding sexual behavior, but more recently pheromones which signal danger to conspecifics have been identified in rodents. In fact, rats are capable of communicating information regarding a specific fear to a companion with which they share a cage. If a rat is trained to associate a previously neutral odor with a foot shock and then pair housed with another rat, the companion will also display a fear response specific to the trained odor, despite never being shocked itself. This communication relies on pheromones; presenting soiled bedding from a shocked rat to an individually housed naïve rat produces the same fear response in the naïve rat. The current protocol describes how to produce this phenomenon in adult Sprague Dawley rats. It is simple and easily reproduced, requires minimal equipment, and may be completed within one week.

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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.281
GPT teacher head0.335
Teacher spread0.054 · 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
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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