Data from: Microbiome investigation in the ecological speciation context of lake whitefish (Coregonus clupeaformis) using next-generation sequencing
Bibliographic record
Abstract
Few studies have applied NGS methods to investigate the microbiome of vertebrates in their natural environment and in freshwater fishes in particularly. Here, we used pyrosequencing of the 16S gene rRNA to (i) test for differences in kidney bacterial communities (i.e. microbiota) of dwarf and normal whitefish found as sympatric pairs, (ii) test the hypothesis of higher bacterial diversity in normal compared with dwarf whitefish and (iii) test for the occurrence of parallelism with the presence and composition of bacterial communities across species pairs inhabiting different lakes. The kidney microbiota of 253 dwarf and normal whitefish from five lakes was analysed combining a double-nested PCR approach with 454 pyrosequencing. Bacteria were detected in 52.6% of the analysed whitefish. There was no overall significant difference among lakes and forms, although the lake × form interaction was found significant. We identified 579 bacterial genera, which is substantially more than previous descriptions using less sensitive techniques of fish bacterial diversity in kidney, pathogenic or not. Ten of these genera contained eighteen pathogenic species. Differences in bacteria composition between whitefish forms were not parallel among lakes. In accordance with the higher diversity of prey types, normal whitefish kidney tissue consistently had a more diverse bacterial community and this pattern was parallel among lakes. These results add to building evidence from previous studies on this system that the adaptive divergence of dwarf, and normal whitefish has been driven by both parallel and nonparallel ecological conditions across lakes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".