Multivariate statistical analyses of rDNA and rRNA fingerprint data to differentiate microbial communities in swine manure
Bibliographic record
Abstract
Fingerprint data from swine manure microbial community rRNAs and rRNA genes were treated by multivariate statistical and diversity analyses to differentiate swine manures. Microbial communities from finishing pig manure and from a mixture of manure slurries from maternity confinement and finishing pigs were characterized using a combination of amplicon length heterogeneity PCR (LH-PCR) and terminal restriction fragment length polymorphism (T-RFLP), using PCR primers targeting Bacteria and Archaea, respectively. Unweighted pair group method with arithmetic mean clustering, principal components analysis (PCA), indicator species analysis (ISA), and diversity analyses showed that rRNA-based fingerprinting methods [reverse transcription (RT)-LH-PCR and RT-T-RFLP] were more effective than rDNA-based fingerprinting methods for distinguishing the manure samples. Multiresponse permutation procedure from fingerprint data showed that all manure samples had distinct microbial communities. PCA and ISA showed that the major phylotypes differentiating the LH-PCR or the RT-LH-PCR profiles were distributed differently between manures, suggesting that the bacterial community structure was different from the metabolically active bacterial community. The detection of minor archaeal populations was greater using RT-T-RFLP instead of T-RFLP. The findings indicated that the analysis of microbial community rRNAs could differentiate each manure sample from the others and would be appropriate for the monitoring of metabolically active populations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".