Metal Distribution and Sediment Quality Variation across Sediment Depths of a Subtropical Ramsar Declared Wetland
Bibliographic record
Abstract
The study of wetlands is particularly important as these systems act as natural water purifiers and thus can act as sinks for contaminated particles. Wetland sediments are important as they provide an indication of potential contamination across temporal and spatial scales. The current study aimed to investigate the distributions of selected metals and nutrients in different sites in relation to sediment depth, and identify relationships among sediment metals. Significant differences in nutrient (i.e., N, P) and metal (i.e., K, Mg, Na, Fe, Cu, B) concentrations were found across study sites, whereas nutrients (i.e., N, P) and metals (i.e., Ca, Mg, Fe, Cu, Zn) were significantly different with sediment depths. When compared against Canadian sediment standards, most of the assessed metals were within the “no effect” level across the different sites and depths. The K, Ca, and Mg concentration showed extreme contamination across all sites and depths. The enrichment factor values for K, Ca, and Mg showed extremely high enrichment levels for all sites and sediment depths. The Na, Mn, Fe, Cu, Zn, and B concentration showed mostly background enrichment levels. All sediments across the different sites and sediment depths indicated deterioration of sediment quality. Pearson correlations suggest that most metals might have originated in a similar source as that of Mn and B, owing to a lack of significant differences. These results provide baseline information for the general management of the Nylsvley Wetland in relation to sediment metal pollution. The specific sources of metal contaminants also require further elucidation to further inform management efforts.
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 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) | 0.001 | 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 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".