Forward Explicit Congestion Notification (FECN) for Datacenter Ethernet Networks
Bibliographic record
Abstract
Microbiota analyses are key to understanding the bacterial communities within dairy cattle, but the impact of different storage conditions on these analyses remains unclear. This study sought to examine the effects of freezing at -80°C immediately after collection, refrigeration at 4°C for three days and seven days and absolute ethanol preservation on the microbiota diversity of pooled fecal samples from dairy cattle. Examining 16S rRNA gene sequences, alpha (Shannon, Pielou evenness, observed features and Faith PD indices) and beta (Bray-Curtis, βw and Weighted UniFrac) diversity were assessed. The effects of storage conditions on these metrics were evaluated using linear mixed models and PERMANOVA, incorporating the farm as a random effect. Our findings reveal that 7d and E significantly altered the Shannon index, suggesting a change in community composition. Changes in Pielou evenness for 3d and 7d storage when compared to 0d were found, indicating a shift in species evenness. Ethanol preservation impacted both observed features and Faith PD indices. Storage conditions significantly influenced Bray-Curtis, βw, and Weighted UniFrac metrics, indicating changes in community structure. PERMANOVA analysis showed that these storage conditions significantly contributed to microbiota differences compared to immediate freezing. In conclusion, our results demonstrate that while refrigeration for three days had minimal impact, seven days of refrigeration and ethanol preservation significantly altered microbiota analyses. These findings highlight the importance of sample storage considerations in microbiota research.
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".