Neuronal plasticity and seasonal reproduction in sheep
Bibliographic record
Abstract
Seasonal reproduction represents a naturally occurring example of functional plasticity in the adult brain as it reflects changes in neuroendocrine pathways controlling gonadotropin-releasing hormone (GnRH) secretion and, in particular, the responsiveness of GnRH neurons to estradiol negative feedback. Structural plasticity within this neural circuitry may, in part, be responsible for seasonal switches in the negative feedback control of GnRH secretion that underlie annual reproductive transitions. We review evidence for structural changes in the circuitry responsible for seasonal inhibition of GnRH secretion in sheep. These include changes in synaptic inputs onto GnRH neurons, as well as onto dopamine neurons in the A15 cell group, a nucleus that plays a key role in estradiol negative feedback. We also present preliminary data suggesting a role for neurotrophins and neurotrophin receptors as an early mechanistic step in the plasticity that accompanies seasonal reproductive transitions in sheep. Finally, we review recent evidence suggesting that kisspeptin cells of the arcuate nucleus constitute a critical intermediary in the control of seasonal reproduction. Although a majority of the data for a role of neuronal plasticity in seasonal reproduction has come from the sheep model, the players and principles are likely to have relevance for reproduction in a wide variety of vertebrates, including humans, and in both health and disease.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".