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Record W1971710907 · doi:10.1074/jbc.r110.154476

Translational Control Mechanisms in Long-lasting Synaptic Plasticity and Memory

2010· review· en· W1971710907 on OpenAlexaff
Christos G. Gkogkas, Nahum Sonenberg, Mauro Costa‐Mattioli

Bibliographic record

VenueJournal of Biological Chemistry · 2010
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeuroscienceMnemonicMemory formationSynaptic plasticityMetaplasticityLong-term memoryMemory consolidationNeuroplasticityShort-term memoryComputer sciencePsychologyBiologyCognitive psychologyHippocampusWorking memoryCognition

Abstract

fetched live from OpenAlex

Mnemonic processes are controlled by selective modification (weakening or strengthening) of connections between neurons (1–5). To understand the precise molecular mechanisms by which this remarkably complex network encodes a given episode during learning is arguably one of the major challenges in modern neuroscience (6). Two kinds of memory storage mechanisms have been described: short-term memory (STM),3 which lasts only a few minutes or hours, and long-term memory (LTM), which persists for many weeks, months, years, and even a lifetime (7).

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.004

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 head0.100
GPT teacher head0.369
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations75
Published2010
Admission routes1
Has abstractyes

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