Fertility‐associated metabolites in bull seminal plasma and blood serum: <sup>1</sup>H nuclear magnetic resonance analysis
Bibliographic record
Abstract
Early estimation of bull fertility is highly desirable for the conservation of male genetics of endangered species and for the exploitation of genetically superior sires in artificial insemination programs. The present work was conducted as a proof-of-principle study to identify fertility-associated metabolites in dairy bull seminal plasma and blood serum using proton nuclear magnetic resonance ((1)H NMR). Semen and blood samples were collected from high- and low-fertility breeding bulls (n = 5 each), stationed at Semex, Guelph, Canada. NMR spectra of serum and seminal plasma were recorded at a resonance frequency of 500.13 MHz on a Bruker Avance-500 spectrometer equipped with an inverse triple resonance probe (TXI, 5 mm). Spectra were phased manually, baseline corrected, and calibrated against 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid at 0.0 parts per million (ppm). Spectra were converted to an appropriate format for analysis using Prometab software running within MATLAB. Principal component analysis was used to examine intrinsic variation in the NMR data set, and to identify trends and to exclude outliers. Partial least square-discriminant analysis was performed to identify the significant features between fertility groups. The fertility-associated metabolites with variable importance in projections (VIP) scores >2 were citrate (2.50 ppm), tryptamine/taurine (3.34-3.38 ppm), isoleucine (0.74 ppm), and leucine (0.78 ppm) in the seminal plasma; and isoleucine (1.14 ppm), asparagine (2.90-2.94 ppm), glycogen (3.98 ppm), and citrulline (1.54 ppm) in the serum. These metabolites showed identifiable peaks, and thus can be used as biomarkers of fertility in breeding bulls.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".