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Record W2064386067 · doi:10.1080/10253890310001638136

Anxiety in Rats Selectively Bred for Fast and Slow Kindling Rates: Situation-Specific Outcomes

2003· article· en· W2064386067 on OpenAlexaff
Owen Kelly, Judy McIntosh, Dan McIntyre, Zul Merali, Hymie Anisman

Bibliographic record

VenueStress · 2003
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsRoyal Ottawa Mental Health CentreCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsKindlingAnxietyPsychologyDiazepamAmygdalaMoro reflexStartle responseAnxiolyticStartle reactionNeuroscienceAnxiogenicStressorElevated plus mazeEpileptogenesisEndocrinologyInternal medicineMedicineStimulationPsychiatryHippocampusReflex

Abstract

fetched live from OpenAlex

Rats selectively bred for amygdala excitability, realized by fast or slow kindling epileptogenesis, were previously reported to exhibit differential levels of anxiety. Although the Slow kindling rats generally appeared more anxious in several behavioral tests, under certain test conditions the Fast kindling rats displayed greater anxiety or stressor reactivity. The present investigation confirmed that in a test of anxiety comprising suppression of consumption of a palatable snack in an unfamiliar environment, the Slow kindling rats exhibited greater anxiety and that this effect was attenuated by diazepam. Likewise, the acoustic startle response was greater in the Slow kindling rats. However, the fear-potentiated startle response was more pronounced in Fast kindling rats, particularly among females, irrespective of whether the test parameters elicited moderate or high startle amplitudes. The elevated startle in the Slow rats, and the fear potentiated startle in the Fast rats, were attenuated by diazepam. These data indicate the need to differentiate subtypes of anxiety in animal models, and raise the issue that anxiety elicited by specific environmental triggers may interact with genetically determined trait characteristics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.158
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.304
Teacher spread0.263 · 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 teacher head, 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

Citations10
Published2003
Admission routes1
Has abstractyes

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