Bibliographic record
Abstract
Joseph LeDoux is a pioneering neuroscientist who has made profound discoveries that continue to impact our understanding of the neural basis of emotion and memory, particularly the role of the amygdala in threat conditioning. LeDoux's trailblazing and elegant studies were some of the first to examine the circuit basis of behavior. His work combined techniques to trace pathways into and out of the amygdala important for threat conditioning and related behaviors. Since that time, these types of circuit tracing studies have exploded in popularity across neuroscience, and I would argue, we all owe a debt to LeDoux for this. LeDoux has made numerous additional contributions to neuroscience and, by bringing emotion back to neuroscience, has helped unite neuroscience with psychology. A gifted writer with a knack for communicating complicated scientific ideas in an accessible manner, LeDoux has become an ambassador of science who uses his love of music to help educate and inspire. Perhaps more important than these laudable scientific achievements, LeDoux is also a true "gentleman" of science, showing that science need not be a contact sport. Here, I give a personal account on why Joseph LeDoux is one of my scientific role models.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
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".