Bibliographic record
Abstract
Thomas Carew has explored the cellular and molecular mechanisms of memory in the model system Aplysia californica.Early in his career, he was a member of the team in Eric Kandel's laboratory that provided the first evidence for both long-term memory and associative learning in Aplysia.These discoveries gave rise to independent behavioral, synaptic, and molecular studies in the Carew laboratory first at Yale, then at the University of California-Irvine, and finally at New York University.In this work, he developed several lines of research that provide mechanistic insights into learning and memory.For example, with his research team, he was able to dissociate several different types of memory on both behavioral and synaptic levels, showing, for example, in contrast to a prevailing theoretical view, that it is possible to induce both intermediate-term and long-term synaptic changes underlying memory in the absence of any short-term changes.He also identified three mechanistically distinct phases of synaptic facilitation in the central nervous system, each of which predicted the existence and molecular features of distinct temporal phases of behavioral memory.The predominant focus of his work for the last several years has centered on two basic themes: first, the critical importance of pattern and timing in memory formation; and, second, the essential roles that growth factors play in the induction of long-term memories.In exploring both of these themes, he has been able to identify the contribution of a number of specific molecular cascades, as well as their interactions, in the induction and consolidation of different forms of memory.The overarching goal of his research has been the forging of direct connections between specific synaptic and molecular events to bona fide instances of memory expressed behaviorally.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.161 | 0.092 |
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".