Ecotoxicological effects of personal care products on the sea urchin <i>Echinometra lucunter</i>
Bibliographic record
Abstract
Personal care products (PCPs) are increasingly studied worldwide to understand better their ecotoxicological effects on different aquatic species. However, studies assessing their ecotoxicological effects on tropical marine aquatic organisms are still scarce. To address this information gap, this study aimed to evaluate the acute and chronic toxicity of three PCPs, methylparaben (MP), propylparaben (PP) and butylhydroxytoluene (BHT), on the tropical sea urchin Echinometra lucunter. The procedures were based on the protocols established by the US Environmental Protection Agency and the Brazilian national standard (ABNT NBR 15350). Predictive computational tools (Opera Qsar and Vega Qsar) were used to evaluate the persistence/biodegradability, bioaccumulation and mobility of PCPs. Acute exposure results showed the following ranking of toxicity: BHT (IC50 = 38.14 mg/l) > PP (IC50 = 73.20 mg/l) > MP (IC50 = 74.47 mg/l). Chronic toxicity tests indicated that BHT presented the lowest IC50 (6.85 mg/l), followed by PP (IC50 = 15.57 mg/l) and MP (IC50 = 20.09 mg/l). Additionally, in silico predictions support the findings related to the potential risks of these PCPs in aquatic ecosystems. The data obtained in this study can support future analyses of environmental risk concerning PCPs and support the establishment of concentration limits in relevant legislation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".