Special issue on the role of translation and transcription in learning and memory
Bibliographic record
Abstract
Special issue on the role of translation and transcription in learning and memoryThe editors of Learning & Memory are pleased to bring you this special issue covering the role of translation and transcription in learning and memory.For decades it has been accepted that long-term memory requires protein synthesis, and that synaptic plasticity supports learning and memory.In addition, data have accumulated indicating that translation of mRNA at the synapse plays a major role in synaptic plasticity, and more recently, increasing evidence suggests that disregulation of transcription/ translation pathways contributes to psychiatric and substance abuse disorders, and other disorders affecting learning and memory.This special issue brings together eight research and review articles from leaders in the field and covers this topic from be-havioral to molecular approaches.We believe this issue represents an important contribution to the field, and it will be featured at the annual meeting of the Pavlovian Society in Vancouver, BC, the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".