Proposal of novel Predicted No Effect Concentrations (PNEC) for metals in freshwater using Species Sensitivity Distribution for different taxonomic groups
Bibliographic record
Abstract
Water pollution by metals and metalloids promotes toxic effects to aquatic biota especially in mining regions. Environmental legislation applied to protect aquatic life from the toxicity of metals relies on the definition of protective values (PVs) for each compound. Among methods used to define PVs, Species Sensitivity Distribution (SSD) curves enable the derivation of the Predicted No Effect concentration (PNEC). In this context, this is one of the first studies to propose the construction of acute and chronic split SSD curves built separately for three groups of freshwater organisms (algae, invertebrates and fish) to derive PNEC values for the 14 metals most commonly observed in iron ore mining tailings. Data used to construct split SSD curves were derived from the USEPA ECOTOX knowledgebase and EnviroTox databases and segregated according to the freshwater organism group and as “acute” or “chronic” toxicity. Then, split SSD curves were built using a minimum of nine species for each group to determine the hazardous concentration to 5% of species (HC 5 ) and PNEC values for each group. Once PNEC were derived, a framework was proposed to calculate the Bioavailabity Factor (BioF) used to adjust values for local bioavailability conditions considering water quality characteristics in different regions. The lowest acute PNEC were observed for algae and invertebrates and corresponded to Silver (Ag). Nearly half of calculated PNEC were below current PVs in practice in Brazil, United States (US), United Kingdom (UK), Canada and European Union (EU). Results reinforce the pertinence of: (i) splitting SSD curves to define PVs for metals; and (ii) taking bioavailability into consideration to correct PNEC for local conditions. In addition, outcomes suggest that it is critical to rethink PVs related to metals for aquatic life protection, mainly in Brazil and Minas Gerais state, a region known for extensive mining activity. Finally, PNEC values obtained in this study may be used for ecological risk assessment studies, especially in areas affected by mining and other activities that result in pollution by metals and metalloids, such as Brazil.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".