Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic
Bibliographic record
Abstract
Marine microbes play essential roles in global energy and nutrient cycles. A primary method of determining their diversity and distribution is through sequencing of 16S ribosomal RNA genes from environmental samples. However, the perceived community composition may vary significantly based on differences in methodology, including choice of 16S variable region(s). This study investigated the influence of 16S variable region selection (V4-V5 or V6-V8) on perceived community composition and diversity for bacteria, Archaea and chloroplasts by tag-Illumina sequencing. We used 24 samples from the photic zone of the Scotian Shelf, northwest Atlantic, collected during a spring phytoplankton bloom. Taxonomic assignment and community composition varied greatly depending on the choice of variable regions while observed patterns of beta diversity were reproducible between variable regions. V4-V5 was considered the preferred variable region for future studies based on its superior recognition of Archaea, which has received little attention in bloom dynamics. The V6-V8 region captured more of the bacterial diversity, including the abundant SAR11 clades and, to a lesser extent, that of chloroplasts. However, the magnitude of difference between variable regions for bacteria and chloroplast was less than for Archaea.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".