Urban stressors impact lipid profiles in freshwater periphytic communities
Bibliographic record
Abstract
The composition of lipids in algae are significantly influenced by environmental factors, including light intensity. Exposure to organic and inorganic contaminants can also disrupt the synthesis of fatty acids, changing the lipid composition of microalgae in periphytic communities. In this study, we looked at how a biocide such as dodecylbenzyldimethylammonium chloride (BAC 12) and two photoperiod durations can affect a biofilm's polar lipidome in a microcosm experiment. The heterotrophic compartment appeared to be raised by exposure to BAC 12 at the expense of phototrophic organisms. Additionally, the overall decline in polyinsaturated fatty acids indicated that the biofilm's phototrophic organisms were all severely impacted. However, it may be difficult to differentiate the effects of contamination from those of light, since there was no observable effect of photoperiods on the conventional fatty acid determination. The molecular species composition of both glycolipids and phospholipids was investigated in additional multivariate analyses. It was suggested that some molecular species may serve as more specific markers of light duration at the biofilm scale. Lastly, we recommend applying a similar lipidomic approach with monospecific cultures of microalgal strains in future research to support these findings, as the methodology used in this study would be applicable to other biofilm-derived microorganisms.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".