Estuarine mixing behavior of colloidal organic carbon and colloidal mercury in Galveston Bay, Texas
Bibliographic record
Abstract
Mercury (Hg) in estuarine water is distributed among different physical phases (i.e. particulate, colloidal, and truly dissolved). This phase speciation influences the fate and cycling of Hg in estuarine systems. However, limited information exists on the estuarine distribution of colloidal phase Hg, mainly due to the technical difficulties involved in measuring it. In the present study, we determined Hg and organic carbon levels from unfiltered, filtered (<0.45 μm), colloidal (10 kDa-0.45 μm), and truly dissolved (<10 kDa) fractions of Galveston Bay surface water in order to understand the estuarine mixing behavior of Hg species as well as interactions of Hg with colloidal organic matter. For the riverine end-member, the colloidal fraction comprised 43 ± 11% of the total dissolved Hg pool and decreased to 17 ± 8% in brackish water. In the estuarine mixing zone, dissolved Hg and colloidal organic carbon showed non-conservative removal behavior, particularly in the low salinity (<15 ppt) region. This removal may be caused by salt-induced coagulation of colloidal matter and consequent removal of dissolved Hg. The particle-water interaction, K(d) ([particulate Hg (mol kg(-1))]/[dissolved Hg (mol L(-1))]) of Hg decreased as particle concentration increased, while the particle-water partition coefficient based on colloidal Hg and the truly dissolved Hg fraction, K(c) ([colloidal Hg (mol kg(-1))]/[truly dissolved Hg (mol L(-1))]) of Hg remained constant as particle concentration increased. This suggests that the particle concentration effect is associated with the amount of colloidal Hg, increasing in proportion to the amount of suspended particulate matter. This work demonstrates that, colloidal organic matter plays an important role in the transport, particle-water partitioning, and removal of dissolved Hg in estuarine waters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".