Factors affecting methyl mercury partitioning to DOC and UVB photodegradation in fresh waters
Bibliographic record
Abstract
The partitioning of methyl mercury (MeHg) to dissolved organic carbon (DOC) and the photodegradation of mercury by UVB radiation (280-320nm) are two important processes that influence the availability of MeHg to the base of the aquatic food chain. Water samples from 20 sites across Eastern Ontario and Western Quebec were filtered sequentially using tangential flow ultrafiltration to determine the size distribution of MeHg and DOC and to test whether the concentrations and distribution of these two variables varied in wetlands, lakes and rivers. These filtrates were also analyzed for DOC fluorescence and absorbance. The highest proportions of mean MeHg (47.3 +/- 25.4%), DOC (56.8 +/- 14.5%) and DOC FL (74.5 +/- 11.4%) were found in the low molecular weight fractions (<5 kDa). Significant differences in the distribution and concentration of MeHg amongst filtered samples were found between wetlands, lakes and rivers. MeHg was related to DOC at all size fractions. The low molecular weight organic compounds may be an important contributor to MeHg biomagnification through uptake by bacteria and/or algae. St. Lawrence river water was collected to test factors that affect the rate of photodegradation of MeHg. Samples exposed to UVB irradiance from a fluorescent lamp and spiked with MeHg(5 ng/L) illustrated significant decreases in concentration with a 31% average loss after 6 hours. No significant difference in photodegradation was found between samples with and without added Fe(II). MeHg concentrations decreased 35.4% and 41.7% after 6 hours of exposure at pH 3 and 5, respectively. It appears that photo-demethylation is a function of UVB exposure, is more rapid in acidic conditions and likely occurs slower under most natural freshwater conditions due to the attenuation of UVB.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".