Uptake of Dissolved Organic Carbon-Complexed <sup>65</sup>Cu by the Green Mussel <i>Perna viridis</i>
Bibliographic record
Abstract
Stable Cu isotope ((65)Cu) was complexed with various representative dissolved organic carbon (DOC) types, including coastal seawater DOC, fulvic acid (FA), cyanobacteria spirulina (SP) DOC, histidine (His), cysteine (Cys), and lipophilic diethyl dithiocarbamate (DDC) at different concentrations. The uptake of these dissolved Cu species by the coastal green mussel Perna viridis was quantified for the first time. Copper complexed with different DOC types were taken up in some measure by mussels, depending on the DOC types. However, complexation generally reduced Cu uptake as compared to that of inorganic Cu species, and DOC type-specific negative relationships were found between DOC levels and Cu uptake. Strong Cu binding sites (including His and organic sulfur functional groups) within DOC appeared to control the inhibitory effects of DOC on Cu uptake, possibly due to the competitive binding of Cu between the dissolved phase and biological membranes. Therefore, differences in strong Cu binding site levels may explain the differences in bioavailability of Cu complexed with different types of DOC. At the same time, the variations in Cu-DOC uptake may also be partly attributed to the absorption of Cu-DOC complexes, especially for the small Cu-DOC complexes (e.g., Cu-Cys, Cu-His, or Cu-DDC). Our study highlights the importance of considering the specificity of Cu-DOC complexes when assessing biological exposure to dissolved Cu in natural waters, especially during events, such as phytoplankton bloom periods, that could modify DOC composition and concentrations.
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".