Comparative transcriptomic analysis reveals candidate genes for seasonal breeding in the male Lion-Head goose
Bibliographic record
Abstract
1. Due to seasonal breeding, geese breeds from Southern China have low egg yield. The genetic makeup underlying performance of local breeds is largely unknown, and few studies have investigated this problem. This study integrated 21 newly generated and 50 publicly existing RNA-seq libraries, representing the hypothalamus, pituitary and testis, to identify candidate genes and importantly related pathways associated with seasonal breeding in male Lion-Head geese. 2. In total, 19, 119 and 302 differentially expressed genes (DEGs) were detected in the hypothalamus, pituitary and testis, respectively, of male Lion-Head geese between non-breeding and breeding periods. These genes were significantly involved in the neuropeptide signalling pathway, gland development, neuroactive ligand-receptor interaction, JAK-STAT signalling pathway, cAMP signalling pathway, PI3K-Akt signalling pathway and Foxo signalling pathway. 3. By integrating another 50 RNA-seq samples 4, 18 and 40 promising DEGs were confirmed in hypothalamus, pituitary and testis, respectively. 4. HOX genes were identified as having important roles in the development of testis between non-breeding and breeding periods of male Lion-Head geese.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".