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Record W2748370252 · doi:10.5539/ijb.v9n4p10

Naloxone’s Effect on Activity-Induced Phase Shifts of Circadian Rhythms

2017· article· en· W2748370252 on OpenAlexvenueno aff
Daniel Janik, Margaret Alvarez Brereton

Bibliographic record

VenueInternational Journal of Biology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersUniversity of Wisconsin-Eau ClaireNational Science Foundation
Keywords(+)-NaloxoneCircadian rhythmEndocrinologyInternal medicineAntagonistRhythmChemistryBiologyReceptorMedicine

Abstract

fetched live from OpenAlex

Vigorous running in a novel exercise wheel by hamsters during the subjective day results in large (about 3 hr) phase advances of their circadian rhythms. We administered the opiate receptor antagonist naloxone at 10 mg/kg and 20 mg/kg (i.p.) and found that at 20 mg/kg it significantly reduced phase shifts. This dose of naloxone also reduced the amount of running shown by hamsters in the novel wheel. At the 20 mg/kg dose, the reduced amount of running resulted in smaller phase shifts. The results are consistent with the hypothesis that nonphotic phase shifts are linked to exercise and arousal in hamsters. However, because high amounts of running were accompanied by phase shifting in animals whose opiate receptors were blocked, the results provide no evidence that the rewarding nature of running in rodents is an important causal factor in nonphotic phase shifting beyond its role in promoting running.

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.364
Teacher spread0.319 · 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
Published2017
Admission routes1
Has abstractyes

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