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Record W2257672364 · doi:10.1038/srep19850

Acute fluoxetine exposure alters crab anxiety-like behaviour, but not aggressiveness

2016· article· en· W2257672364 on OpenAlexafffund
Trevor J. Hamilton, Garfield T. Kwan, Joshua Gallup, Martín Tresguerres

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsMacEwan UniversityWomen and Children’s Health Research Institute
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluoxetineAggressionSerotoninAntidepressantAnxietySerotonin reuptake inhibitorParoxetineNeurotransmitterPsychologyInternal medicineEndocrinologyMedicinePsychiatryCentral nervous system

Abstract

fetched live from OpenAlex

Aggression and responsiveness to noxious stimuli are adaptable traits that are ubiquitous throughout the animal kingdom. Like vertebrate animals, some invertebrates have been shown to exhibit anxiety-like behaviour and altered levels of aggression that are modulated by the neurotransmitter serotonin. To investigate whether this influence of serotonin is conserved in crabs and whether these behaviours are sensitive to human antidepressant drugs; the striped shore crab, Pachygrapsus crassipes, was studied using anxiety (light/dark test) and aggression (mirror test) paradigms. Crabs were individually exposed to acute doses of the selective serotonin reuptake inhibitor, fluoxetine (5 or 25 mg/L), commonly known as Prozac®, followed by behavioural testing. The high dose of fluoxetine significantly decreased anxiety-like behaviour but had no impact on mobility or aggression. These results suggest that anxiety-like behaviour is more sensitive to modulation of serotonin than is aggressiveness in the shore crab.

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 score0.006
Threshold uncertainty score0.011

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 studies0.0000.000
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 head0.025
GPT teacher head0.288
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 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

Citations48
Published2016
Admission routes2
Has abstractyes

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