Bibliographic record
Abstract
Studies of brain-damaged patients have provided remarkable insights into how our memories are organized (1). In particular, these studies have established that memory consolidation is a dynamic process involving gradual (but quite dramatic) reorganization of the circuits supporting memory over time (2, 3). A pioneer in this field was the French psychologist Theodule Ribot. In the late part of the 19th century, Ribot described how memory loss after brain insult tended to be related to the age of the memory: the effect on more recent memories was typically greater than that on older (or more remote) memories (4). Later studies established that damage to the medial temporal lobe (5), and more specifically the hippocampus (1), is responsible for this typical graded amnesia. This Ribot gradient has suggested that the hippocampus is only temporarily involved in the storage and recall of certain types of memory, and that these functions must be subsumed by extrahippocampal structures as memories mature (2, 6). Hippocampal damage in experimental animals also preferentially disrupts recent memory. Such examples, from mice to monkeys, suggest that time-dependent memory reorganization is an evolutionarily conserved process for memory consolidation (3). However, differences do exist between human and experimental animal studies, and these differences have made it difficult to come up with a unified mechanistic account of memory consolidation. For example, the lengths of the gradients vary dramatically from days or weeks (for example, in rodents) to years (in humans), suggesting that the rate of reorganization may vary greatly across species. Second, although studies in experimental animals have begun to successfully identify extrahippocampal brain regions that support remote memory recall, the evidence from human studies of remote memory has been much less consistent, and it has even been suggested that autobiographical memories may never become truly independent of the …
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".