Heavy-metal resistance in Gram-negative bacteria isolated from Kongsfjord, Arctic
Bibliographic record
Abstract
Isolation and characterization of heterotrophic Gram-negative bacteria was carried out from the sediment and water samples collected from Kongsfjord, Arctic. In this study, the potential of Arctic bacteria to tolerate heavy metals that are of ecological significance to the Arctic (selenium (Se), mercury (Hg), cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn)) was investigated. Quantitative assay of 130 isolates by means of plate diffusion and tube dilution methods was carried out by incorporation of different concentrations of metals. Growth in Se and Pb at a concentration of 3000 μg/L was significantly lower (P≤0.0001) than at 2000 μg/L. The minimum inhibitory concentration for Cd and Hg was 50 μg/L (P≤0.0001, F=264.23 and P≤0.0001, F=291.08, respectively) even though in the tube dilution test, Hg-containing tubes showed much less growth, revealing its superior toxicity to Cd. Thus, the level of toxicity of heavy metals was found to be in the order of Hg>Cd>Cu>Zn>Pb>Se. Multiple-metal-resistant isolates were investigated for their resistance against antibiotics, and a positive correlation was observed between antibiotic and metal resistance for all the isolates tested. The resistant organisms thus observed might influence the organic and inorganic cycles in the Arctic and affect the ecosystem.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".