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Record W2085897557 · doi:10.1155/2012/581291

Molecular Determinants of the Spacing Effect

2012· review· en· W2085897557 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNeural Plasticity · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsCREBProtein kinase APhosphataseTranscription factorPhosphorylationProtein tyrosine phosphataseProtein kinase CMAPK/ERK pathwayCell biologyBiologyKinaseNeuroscienceBiochemistryGene

Abstract

fetched live from OpenAlex

Long-term memory formation is sensitive to the pattern of training sessions. Training distributed over time (spaced training) is superior at generating long-term memories than training presented with little or no rest interval (massed training). This spacing effect was observed in a range of organisms from invertebrates to humans. In the present paper, we discuss the evidence supporting cyclic-AMP response element-binding protein 2 (CREB), a transcription factor, as being an important molecule mediating long-term memory formation after spaced training. We also review the main upstream proteins that regulate CREB in different model organisms. Those include the eukaryotic translation initiation factor (eIF2α), protein phosphatase I (PP1), mitogen-activated protein kinase (MAPK), and the protein tyrosine phosphatase corkscrew. Finally, we discuss PKC activation and protein synthesis and degradation as mechanisms by which neurons decode the spacing intervals.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.348
Teacher spread0.316 · 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