EFfects of Diesel and Interactions with Copper and Other Metals in an Estuarine Sediment Microbial Community
Bibliographic record
Abstract
Estuarine sediment microcosms were treated with combinations of diesel, copper (at two levels), and a mixture of heavy metals (mercury, cadmium, lead, and chromium; at two levels) mimicking the contaminant loadings found in harbor sediments. The effects on the microbial community were monitored by polar lipid fatty acid analysis. Diesel addition increased microbial biomass, caused shifts in some fatty acid structural groups, and decreased starvation biomarkers. Incorporation of diesel hydrocarbons into lipids was expressed as an increase in the proportion of odd-carbon-number fatty acids. No treatment with the metals mixture (mercury, cadmium, lead, and chromium) alone significantly changed any parameter derived from the polar lipid fatty acids, but the increase in microbial biomass from diesel addition was higher with the metals mixture, possibly because of indirect effects caused by reductions in grazing resulting from metal-induced toxicity to bacteriovorous nematodes. Copper also modified the effects of diesel addition, preventing biomass increase but not diesel degradation, suggesting that some of the energy gained from diesel oxidation was expended combating copper toxicity. In the present study, observations indicate that metals in general, and copper in particular, can modify the response of sedimentary microorganisms to petroleum-hydrocarbon contaminants.
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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 | 0.000 | 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".