effect of tetracycline on the colonization and growth of microbes on Scirpus lacustris litter in oligotrophic and eutrophic waters
Bibliographic record
Abstract
Studies of microbes on Scirpus lacustris submerged in river (oligotrophic) and wetland (eutrophic) waters were conducted to determine how nutrient status affected colonization and growth on the same substrate. The antibiotic tetracycline was used to repress bacteria to determine if they inhibited fungal growth. The use of tetracycline was also relevant in a broader context as antibiotics are now being detected in aquatic systems. Dead S. lacustris stems were submerged for 33 d in natural and tetracycline amended (500 and 4000 g l -1 ) river and wetland waters. Confocal laser scanning microscopy was used to scan the biofilm while image analysis was used to determine microbial (algal, bacterial, fungal) cell volume and fungal biomass by measuring the length of fungal hyphae. In all treatments, microbial cell volume peaked on Day 10 and was greater (ANOVA, all p < 0.05) than on all other sampling days. Microbial biovolume was higher (p = 0.04) in the wetland vs. river water, possibly because nutrients were not limiting in the wetland. Biofilm thickness was not different between the 2 waters, between treatments or over time (p = 0.7, 0.8 and 0.07, respectively). Fungal biomass was greater (p = 0.01) in the river water compared to the wetland, indicating that the same plant in different aquatic systems will vary in the ratio of bacterial/fungal constituents that colonize it after death. Though there seemed to be a trend of increased fungal biomass in the tetracycline treatments, suggesting bacterial inhibition of fungi, the differences were not statistically significant (p > 0.05). On Day 10, the controls of both water treatments had significantly greater microbial biovolume than both the 500 and 4000 g l -1 antibiotic treatments (both p = 0.02), indicating that tetracycline had a negative effect on microbes colonizing Scirpus.
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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) | 0.001 | 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".