Kinetics of mercury accumulation by freshwater biofilms
Bibliographic record
Abstract
Environmental context Mercury (Hg) is a major environmental contaminant due to its toxicity, accumulation and biomagnification along the food chain. We demonstrate that Hg accumulation by biofilms, one possible entry point for Hg into food webs, is rapid and depends on biofilm structure and composition. These findings have important implications for the understanding of Hg bioavailability and effects towards aquatic microorganisms. Abstract Mercury contamination is of high concern due to its bioaccumulation, toxicity and biomagnification along the food chain. Biofilms can accumulate Hg and contribute to its incorporation in freshwater food webs. Nevertheless, the accumulation kinetics of Hg by biofilms is not well described and understood. The aim of the present study was thus to gain mechanistic understanding of Hg accumulation by biofilms. Kinetics of Hg uptake by biofilms of different ages (e.g. different compositions) was characterised by determining Hg contents in biofilms with and without a cysteine-washing step. Hg accumulation was rapid in both biofilms, with the uptake rate constant of the younger biofilm 10 times higher than that of the older biofilm. Moreover, accumulated Hg reached a plateau at 24 h exposure in the younger biofilm, whereas it increased linearly in the older biofilm. The observed difference in Hg uptake by the studied biofilms is likely a result of the difference in biofilm thickness (and thus Hg diffusion inside the biofilm matrix) and microbial composition. These findings have important implications for the understanding of Hg bioavailability and effects towards aquatic microorganisms.
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) | 0.001 | 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".