Bibliographic record
Abstract
Musk is a scarce and precious medical resource secreted by male forest musk deer (FMD). Current research to promote musk secretion in FMD has used almost exclusively hormone injections, but this approach can be detrimental to the health of FMD. In order to conserve this endangered species as much as possible while increasing the production of musk, this study first used bioinformatics methods to predict the function of quercetin, a flavonoid that promotes testosterone (T) production and prevents late-onset male hypogonadism. On the basis of good prediction effect, different concentrations of quercetin were added to the diet of FMD. The results showed that quercetin could change the levels of T, luteinizing hormone releasing hormone, luteinizing hormone, and estradiol, and regulate the structure of intestinal microorganisms and musk microorganisms of FMD. Moreover, there is a correlation among musk components, hormones, intestinal microorganisms, and musk microorganisms, which indicates that the production of musk may be regulated by these three at the same time, and the addition of quercetin with 800 mg per kg diet could significantly increase the yield of muscone (P < 0.05), the most effective ingredient in musk. In addition, quercetin decreased the high level of cortisol during musk secretion, which may relieve the stress on FMD in this process. This may help to protect the health of FMD. Combined with the results of software prediction, we finally proposed a possible mechanism for the complex process of musk secretion in FMD with a view to providing ideas for further studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".