Data and R code from: GC-MS analysis of murine oestrous odours
Bibliographic record
Abstract
For female mammals, communicating the timing of ovulation is essential for successful reproduction. Urinary volatile organic compounds (VOCs) play a key role in intraspecific communication among many mammals. Using laboratory mice as a model species, we investigated urinary VOCs across the oestrous cycle. We monitored the oestrous stage through daily vaginal cytology assessment and analysed urinary VOCs using headspace gas chromatography-mass spectrometry (GC-MS), testing the utility of portable GC-MS against the more robust benchtop device. We detected 65 VOCs from 40 samples stored in VOC traps and analysed on a benchtop GC-MS and 15 VOCs from 90 samples extracted by solid-phase microextraction (SPME) and analysed on a portable GC-MS. Only three of the identified compounds were found in common between the two techniques. Urine collected from the fertile stages of the oestrous cycle had increased quantities of a few notable VOCs (3,4-dehydro-exo-brevicomin, butanoic acid, pent-1-ene/cyclopentane, 1,2,3-/1,2,4-trimethylbenzene, heptadecane, dioctyl ether, dodecan-1-ol and 2-ethylhexyl salicylate), compared to urine collected from non-fertile stages. However, we did not find differences in the Bray-Curtis chemical dissimilarity indices among oestrous stages. It is possible that the variation in urine VOCs concentration at play during the oestrous cycle was too subtle to be detected by our analytical methods. Overall, the use of the VOC traps combined with benchtop GC-MS was more successful than SPME combined with portable GC-MS in capturing and identifying murine urinary VOCs. Nonetheless, portable GC-MS systems have the potential for some in situ applications since they allow for the immediate interpretation of results, especially at remote field sites.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.361 | 0.198 |
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".