A comparative study of the cytometric characteristics of High and Low nucleic‐acid bacterioplankton cells from different aquatic ecosystems
Bibliographic record
Abstract
Flow cytometry has revealed the existence of two distinct fractions of bacterioplankton cells, characterized by high and low nucleic acid contents (HNA and LNA cells). Although these fractions seem ubiquitous in aquatic systems, little is known concerning the variation in the cytometric parameters used to characterize them. We have performed cytometric analyses of samples from a wide range of aquatic systems to determine the magnitude and variability in the cytometric characteristics of HNA/LNA. We show that neither group is associated to a fixed level of fluorescence and of light scatter. Rather, the relative position of HNA and LNA in the fluorescence versus side scatter cytograms varies greatly, both within and among ecosystems. Although the cytometric parameters of both groups tend to covary, there is often uncoupling between the two, particularly in light scatter. Our results show that, although the basic HNA/LNA configuration is present in most samples, its cytometric expression changes greatly in different ecosystems and along productivity gradients. The patterns in cytometric parameters do not support the simple, dichotomous view of HNA and LNA as active and inactive cells, or the notion of two distinct and independent communities, but rather suggest that there may be cells that are intrinsic to each fraction, as well as others that may exchange between fractions.
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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 | 0.002 | 0.001 |
| 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".