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Record W2996685403 · doi:10.1038/s41598-019-55493-0

Variation in the onset of CO2-induced anxiety in female Sprague Dawley rats

2019· article· en· W2996685403 on OpenAlexafffund
Lucía Améndola, Anna S. Ratuski, Daniel M. Weary

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnxietyVariation (astronomy)PsychologyMedicineBiologyPhysiologyEndocrinologyClinical psychologyInternal medicinePsychiatryPhysics

Abstract

fetched live from OpenAlex

Abstract Carbon dioxide (CO2) is commonly used to kill laboratory rats. Rats find CO2aversive and aversion varies between individuals, indicating that rats vary in CO2sensitivity. Healthy humans experience feelings of anxiety at concentrations similar to those avoided by rats, and these feelings are diminished by the administration of benzodiazepines. Our aim was to assess the effects of the benzodiazepine midazolam on individual thresholds of rat aversion to CO2. Six female Sprague Dawley rats were repeatedly exposed to CO2gradual-fill in approach-avoidance testing. The first three exposures were to a control-treatment followed by three exposures to midazolam (0.375 mg/kg). Within each treatment aversion to CO2was not affected by exposure number; however, tolerance increased from an average of 10.7% CO2avoided during control sessions, to 15.5% CO2avoided when treated with midazolam. These results indicate that rats experience anxiety when exposed to CO2, and that variation in rat CO2sensitivity is driven by individual differences in the onset of these feelings of anxiety. No rat tolerated CO2concentrations required to induce loss of consciousness.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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Same venueScientific Reports→Same topicStress Responses and Cortisol→French-language works237,207→