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Record W3004019557 · doi:10.1080/09291016.2020.1716557

Learning and memory in a rat model of social jetlag that also incorporates mealtime

2020· article· en· W3004019557 on OpenAlexfundno aff
Leanna M. Lewis, Scott H. Deibel, Jillian Cleary, Kayla Viguers, Karen Jones, Darlene M. Skinner, Darcy Hallett, Christina M. Thorpe

Bibliographic record

VenueBiological Rhythm Research · 2020
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCircadian rhythmRhythmHippocampal formationCognitionPsychologyNeuroscienceAnimal modelCircadian clockDiseaseCognitive psychologyDevelopmental psychologyPhysiologyMedicineEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Given the increasing role of circadian rhythm disruption in health and disease, animal models are necessary to elucidate the mechanisms and effects involved. Social jetlag is a mild form of chronic circadian rhythm disruption that involves a misalignment between one’s internal time and their external schedule. Using phase advances and delays that are characteristic of a typical working week with sleep binging on the weekend, we propose an animal model of social jetlag. We also investigate the role of the food entrainable oscillator in learning and memory by manipulating the regularity and number of daily meals. We hypothesized that rats exposed to social jetlag would display cognitive impairments. It was unclear if a consistent meal would ameliorate the deleterious effects of social jetlag. Rats exposed to either social jetlag or unpredictable meals had impaired hippocampal-dependent memory. Activity data suggest that the social jetlag paradigm was unentrainable. Our social jetlag paradigm is a useful model of circadian misalignment that impairs cognition by rapidly uncoupling circadian rhythms from the environment.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.055
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.260
GPT teacher head0.374
Teacher spread0.114 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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