Stabilization of swine faecal samples influences taxonomic and functional results in microbiome analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Studies on the microbiome of different species are on the rise, due to a growing interest in animal health and the safety of food products of animal origin. A challenge with studying animals' microbiomes is to find methods that obtain a good representation of the microbial community of interest. Good unbiased sampling protocols are the basis for a solid experimental design, but may need to be done in environments where sample preservation could be difficult. In this study, we evaluate by shotgun sequencing the impact of stabilizing swine faeces samples using a commercial stabilizer (PERFORMAbiome • GUT | PB-200, DNA Genotek). Using stabilizer makes it possible to obtain DNA that is significantly less degraded than when the samples are not stabilized. Also, the results on the taxonomy and on the bacterial functions encoded in the microbiome are impacted by whether or not the samples are stabilized. Finally, the stabilization of samples that had already been frozen and stored at -80°C led to extraction and DNA quality results similar to those obtained from samples that were stabilized before freezing.
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.
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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 it