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Record W4388363011 · doi:10.1016/j.ibneur.2023.08.1397

UNRAVELING THE MOLECULAR MECHANISMS OF MOTOR LEARNING: THE ROLE OF STRIATAL AKT3 IN ENCODING MOTOR MEMORY

2023· article· en· W4388363011 on OpenAlexaff
Karen Lagueux, Cyr Michel

Bibliographic record

VenueIBRO Neuroscience Reports · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsEncoding (memory)NeuroscienceMotor learningEncoding specificity principlePsychologyCognitive scienceMotor skillComputer science

Abstract

fetched live from OpenAlex

Motor memory is responsible for the automatic execution of complex motor tasks that people perform in their daily lives, yet the molecular mechanisms involved are not fully understood. Previous research in mice has shown that the mechanistic target of rapamycin (mTOR) is involved in learning complex motor skills. Protein kinase B (Akt) is closely associated with mTOR signaling and has been shown to play a role in specific nervous system functions. In this study, we investigated the role of Akt in motor learning using the accelerating rotarod, which allows the distinction of the two known phases of learning: the faster and consolidation learning phases.

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 score0.003
Threshold uncertainty score0.009

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 studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.311
Teacher spread0.288 · 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
Published2023
Admission routes1
Has abstractno

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