Assessing the Environmental Risk of Silver Nanoparticles in Aquatic Ecosystems
Bibliographic record
Abstract
As well known, silver nanoparticles (AgNPs) are being increasingly used in many different sectors due to their particular biocidal characteristics.The existence of these substances in many day life products leads to the presence of AgNPs in different environmental compartments.In particular, the aquatic ecosystem is one of the reservoirs that receive more silver nanoparticles.The affectation of different living organisms by AgNPs has been proved through several studies.Therefore, in this paper a methodology to assess the risk of AgNP for the aquatic ecosystems is presented.The methodology is based on the fuzzy logic theory, which is a proved method to deal with variables that have associated uncertainty.This is the case of many of the variables related to the AgNPs.A selection of input and output variables has been carried out after a deep research.Once the variables are identified, the fuzzy inference systems can be established.From inputs such as the size, the shape and the coating of the AgNPs, it is possible to determine the toxicity.This variable together with the media concentration will provide the final assessment of risk.To prove this methodology, a case study based on an accident has been presented.The results show how the risk of AgNPs vary with the pollutant front advance, arriving to a high risk situation.This tool can be adapted to different situations and types of nanoparticles, making a very appropriate for the decision makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".