Accumulation of Copper and Zinc in Periphyton in Response to Dynamic Variations of Metal Speciation in Freshwater
Bibliographic record
Abstract
Although the free ion activity model (FIAM) has been well-established in laboratory studies, there remains the need for field data in order to validate the applicability of this model in natural systems. The objective of this study was to investigate the response of copper and zinc accumulation in periphyton to short-term variations in metal concentration and speciation in freshwater. During heavy rain events, dissolved Cu in the Furtbach stream increased from 40 to 118 nM, while dissolved Zn increased from 45 to 147 nM due to the release of metals from contaminated sediments. Increases in free copper and free zinc ions in the water (from 10(-14) to 10(-11.5) M for Cu2+; from 1 to 15 nM for Zn2+) were observed during the onset of heavy rain events. Periphytic algae collected from artificial substrates had an intracellular copper content (0.2-2.8 micromol/g dry weight (dw)) that varied as a function of the exchangeable copper in the water (labile form) rather than the free Cu2+. Intracellular zinc content (1.5-8.0 micromol/g dw) was found to follow the same trend as the free zinc ion concentration. Adsorbed Cu and Zn on periphyton showed a very dynamic response to variations in dissolved metal concentration. Different concentrations of dissolved manganese during the two time periods may affect the accumulation of zinc and copper by competition for metal uptake.
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) | 0.000 | 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".