Occurrence of C60 and related fullerenes in the Sava River under different hydrologic conditions
Bibliographic record
Abstract
The presence of nanomaterials in the environment has caught the attention of the scientific because of the uncertainties in their fate, mobility and potential toxic effects. However, few studies have determined experimentally their concentration levels in aquatic systems up to date, which complicates the development of an adequate risk assessment. In the present study, the occurrence of ten fullerenes has been assessed in the Sava River (Southeastern Europe): 27 freshwater samples and 12 sediment samples from 12 sampling points have been analysed during two sampling campaigns. C60 was the most ubiquitous fullerene, with concentrations of 8 pg/l–59 ng/l and 108–895 pg/gdw in water and sediments, respectively. Statistically significant differences existed between the levels in 2014 and 2015, which has been attributed to the extreme hydrologic conditions (severe floods and drought, respectively). C70 fullerene has been detected in most of the samples and the fullerene derivatives [6,6]-phenyl C61 butyric acid methyl ester and N‑methyl fulleropyrrolidine have been detected eventually, which highlights that nanotechnology research and development activities are responsible for emitting these emerging contaminants to the environment. The role of diverse potential anthropogenic sources (including oil refinery, general industrial activity, river navigation, urban emissions and nanotechnology) is discussed.
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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 | 0.001 | 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".