Solving the Neurogenesis Puzzle: Looking for Pieces Outside the Traditional Box
Bibliographic record
Abstract
The vast majority of what is considered fact about adult neurogenesis comes from research on laboratory mice and rats: where it happens, how it works, what it does. However, this relative exclusive focus on two rodent species has resulted in a bias on how we think about adult neurogenesis. While it might not prevent us from making conclusions about the evolutionary significance of the process or even prevent us from generalizing to diverse mammals, it certainly does not help us achieve these outcomes. Here, we argue that there is every reason to expect striking species differences in adult neurogenesis: where it happens, how it works, what it does. Species-specific adaptations in brain and behavior are paramount to survival and reproduction in diverse ecological niches and it is naive to think adult neurogenesis escaped these evolutionary pressures. A neuroethological approach to the study of adult neurogenesis is essential for a comprehensive understanding of the phenomenon. Furthermore, most of us are guilty of making strong assertions about our data in order to have impact yet this ultimately creates bias in how work is performed, interpreted, and applied. By taking a step back and actually placing our results in a much larger, non-biomedical context, we can help to reduce dogmatic thinking and create a framework for discovery.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.042 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 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".