Comparison of spatio-temporal dynamics and composition in size-fractionated and unfractionated Northwestern Atlantic microbial communities
Bibliographic record
Abstract
Abstract Size fractionation is a widely applied approach to target specific microbial size ranges, differentiating between larger particle-associated, and smaller free-living microorganisms. To characterize its impact on microbial diversity and its comparability to unfractionated samples, we analyzed 16 weekly ocean samples across five depths during a spring bloom. A universal marker was used to characterize prokaryotes, eukaryotes and chloroplasts comparing single (0.2 µm) or sequential (3 µm and 0.2 µm) filtration. We analyzed unfractionated, fractionated (small and large fractions) and de-fractionated samples. The particle-associated fraction defines the most different community from the other fractions, and combining size fractions (de-fractionating) before or after sequencing produces a community that is most similar to unfractionated samples in terms of composition, and richness dynamics with the exception of very rare taxa. Between 75% and 97% of features are shared, but some discrepancies in relative abundances were unresolved, including for some lineages of free-living Proteobacteria like OM43. The richness trends were consistent, and ANCOM detected at most one significantly different feature between fractionated, and de-fractionated samples, highlighting the similarity in community composition and temporal dynamics between the sets.
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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 | 0.000 |
| 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 | 0.000 | 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".