Combined eukaryotic and bacterial community fingerprinting of natural freshwater biofilms using automated ribosomal intergenic spacer analysis
Bibliographic record
Abstract
Biofilms are complex communities playing an important role in aquatic ecosystems. Automated ribosomal intergenic spacer analysis (ARISA) has been used successfully to explore biofilm bacterial diversity. However, a gap remains to be filled as regards its application to biofilm eukaryotic populations. The aim of this study is to use ARISA to detect eukaryotic population shifts in biofilm. We designed a new set of primers to focus specifically on the ITS1-5.8S-ITS2 region of diatoms and tested it on natural biofilms. Additionally, we tested universal primers, used previously to perform ARISA on fungal communities. Cloning and sequencing showed that the universal primer set amplified various eukaryotes, whereas the new set was diatom specific. The new set amplified a wider variety of diatoms. Therefore, the universal set is appropriate to study the general eukaryotic population shifts in biofilms, whereas the new set is more appropriate to study diatoms specifically. We used both primer sets, along with a bacterial set, to study the population shifts in natural river biofilms. Principal component analysis of the ARISA fingerprints revealed seasonal shifts that did not coincide for bacterial and eukaryotic communities. Therefore, the use of both eukaryotic and bacterial primers provides a useful insight to assess microbial succession in biofilms.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".