Low fractions of active bacteria in natural aquatic communities?
Bibliographic record
Abstract
AME Aquatic Microbial Ecology Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials AME 31:203-208 (2003) - doi:10.3354/ame031203 Low fractions of active bacteria in natural aquatic communities? Erik M. Smith*, Paul A. del Giorgio Dépt des sciences biologiques, Université du Québec à Montréal, CP 8888, succursale Centre Ville, Montréal H3C 3P8, Canada *Email: smith.erik@uqam.ca ABSTRACT: The notion that a significant fraction of individual cells within natural bacterial assemblages is not actively engaged in cellular metabolism, although not a new idea, remains fairly contentious. Different approaches for probing the metabolic activity of individual cells often yield widely divergent estimates of the proportion of active cells, with some methods suggesting very low levels of individual cell activity. We comment on 2 aspects of the current discussions regarding cell-specific activity in natural bacterioplankton. First, the apparent perception that most aquatic bacteria must be active is not uniformly supported by the data. In a systematic survey of the microautoradiography literature, for example, only 4 out of 23 studies reported a mean proportion of active cells in natural communities that was greater than 50%, and the mean across all such studies was 30%. Second, we propose that the problem of describing bacterioplankton single-cell activity is best approached from the viewpoint that there is a nested hierarchy of physiological states within bacterial communities. The lack of agreement among various methods points to the large range of criteria possible for describing metabolic activity in bacteria. In this regard, the discrete, and over-simplistic, notion of 'active' versus 'inactive' is not particularly useful and should be replaced by a conceptual model in which there exists a continuum of possible single-cell activities. KEY WORDS: Bacteria · Cell-specific activity · Physiological diversity · Methodological approaches Full text in pdf format PreviousExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in AME Vol. 31, No. 2. Online publication date: March 13, 2003 Print ISSN: 0948-3055; Online ISSN: 1616-1564 Copyright © 2003 Inter-Research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".