Trace metals partitioning among different sedimentary mineral phases and the deposit-feeding polychaete Armandia brevis
Bibliographic record
Abstract
Trace metals (Cd, Co, Cu, Fe, Mn, Ni, Pb, Zn) were determined in two operationally defined fractions (HCl and pyrite) in sediments from Ensenada and El Sauzal harbors (Mexico). The HCl fraction had significantly higher metal concentrations relative to the pyrite fraction in both harbors, underlining the weak tendency of most trace metals to associate with pyrite. Exceptionally, Cu was highly pyritized, with degrees of trace metal pyritization (DTMP) >80% in both harbors. Dissolved Fe flux measurements combined with solid phase Fe sulfide data indicated that 98 mt of Fe are precipitated as iron sulfides every year in Ensenada Harbor. These Fe sulfides (and associated trace metals) will remain preserved in the sediments, unless they are perturbed by dredging or sediment resuspension. Calculations indicate that dredging activities could export to the open ocean 0.20±0.13 to (0.30±0.56)×10(3) mt of Cd and Cu, respectively, creating a potential threat to marine benthic organisms. Degrees of pyritization (DOP) values in Ensenada and El Sauzal harbors were relatively low (<25%) while degrees of sulfidization (DOS) were high (~50%) because of the contribution of acid volatile sulfide. DOP values correlated with DTMP values (p≤0.001), indicating that metals are gradually incorporated into pyrite as this mineral is formed. Significant correlations were also found between DTMP values and -log(Ksp(MeS)/Ksp(pyr)) for both harbors, indicating that incorporation of trace metals into the pyrite phase is a function of the solubility product of the corresponding metal sulfide. The order in which elements were pyritized in both harbors was Zn≈Mn<Fe<Cd≈Pb<Ni≈Co<<Cu. Lastly, a strong correlation (r(2)=0.87, p<0.01) was found between average reactive trace metal concentrations and metal concentrations measured in Armandia brevis (a deposit-feeding Opheliid polychaete), suggesting that these labile sedimentary metals are preferentially accumulated by the polychaete, making it a useful biomonitor of sedimentary metal exposure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".