Bibliographic record
Abstract
The nature of the brain presents many challenges to its study, from the intricacy of its structure to the minute timescale at which it functions. Traditional research techniques, such as electrophysiological manipulation and pharmacologic intervention, are limited by their inability to operate with both high temporal and spatial resolution. Optogenetics is a novel technology that provides unparalleled specificity in this regard. It allows for control of neural activity with high temporospatial resolution in a manner that does not disrupt the normal physiology of the system. It is an elegant research tool that uses light to control the electrical activity of genetically defined neuron populations with millisecond precision in systems as complex as freely moving live animals. First demonstrated in 2005, it was identified by Nature as the Scientific Method of the Year in 2010 and is currently used by thousands of labs across the world. It has already yielded new discoveries in a variety of neuroscience subfields and will undoubtedly continue to do so. The technology currently exists in a basic science capacity, but has potential for therapeutic application. It is not without its own limitations, but has advantages over more crude alternatives and has proven to be a powerful tool in the hand of the neuroscientist.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".