Influence of zinc on bacterial populations and their proteolytic enzyme activities in freshwater environments: a cross-site comparison
Bibliographic record
Abstract
Temporal responses of indigenous bacterial populations and proteolytic enzyme (i.e., aminopeptidase) activities in the bacterioplankton assemblages from 3 separate freshwater environments were examined after exposure to various zinc (Zn) concentrations under controlled microcosm conditions. Zn concentrations (ranging from 0 to 10 μmol/L) were added to water samples collected from the Kalamazoo River, Rice Creek, and Huron River and examined for bacterial abundance and aminopeptidase activities at various time intervals over a 48 h incubation period in the dark. The results showed that the Zn concentrations did not significantly influence total bacterial counts directly; however, aminopeptidase activities varied significantly to increasing zinc treatments over time. Also, analysis of variance and linear regression analyses revealed significant positive relationships between bacterial numbers and their hydrolytic enzyme activities, suggesting that both probably co-vary with increasing Zn concentrations in aquatic systems. The results from this study serve as additional evidence of the ecological role of Zn as an extracellular peptidase cofactor on the dynamics of bacterial assemblages in aquatic environments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".