effect of tetracycline on the colonization and growth of microbes on Scirpus lacustris litter in oligotrophic and eutrophic waters
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it