Editorial: The next generation of tools and technologies for studying human neurons in a dish
Bibliographic record
Abstract
Despite their prevalence, disorders of the brain, including neurodevelopmental, neurodegenerative, and other mental illnesses, have historically been some of the most challenging to treat. The enormous economic burden coupled with a paucity of curative therapies highlights an urgent need for new approaches and targets to tackle these diseases. One biologically relevant and transformative method that has been employed is the use of induced pluripotent stem cells (iPSCs) to generate neurons that closely mimic those found within the human brain (Mohamed et al., 2019). Advances in this area have enabled investigators to readily grow many of the cell types found within the human brain on a dish, increasing our understanding of mechanisms and targets that are implicated in these diseases and helping to facilitate translational efforts (Fermini et al., 2018).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.020 |
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".