Spatio-Temporal Assessment of Manganese Contamination in Relation to River Morphology: A Study of the Boac and Mogpog Rivers in Marinduque, Philippines
Bibliographic record
Abstract
This study investigates the contribution of river morphology towards the occurrence of manganese (Mn) in both sediment and surface water (SW), considering the temporal climatic and spatial conditions. The Boac and Mogpog rivers on the island province of Marinduque, Philippines, were examined in this study. These rivers are downstream of the two abandoned open mine pits at San Antonio and Tapian, where mining disasters occurred in 1993 and 1996, respectively. Field sampling programs were conducted in 2019, 2021 and 2022 to measure the Mn concentrations in sediment and SW, and the physicochemical parameters in SW during the same sampling event. Geographic Information System (GIS) tools were employed to characterize the morphology of each river, specifically river slope, river bends, sinuosity, and channel width and length. The Boac and Mogpog rivers were divided into 22 and 15 river segments, respectively, to account for spatial heterogeneity of all parameters. Correlation (r) analysis on the average Mn concentration and river morphology within each segment was performed and indicated that river bends (Boac r = 0.421, Mogpog r = 0.356) and sinuosity (Boac r = 0.403, Mogpog r = 0.352) had the highest correlation with Mn concentrations in sediment. While river slope (Boac r = 0.716, Mogpog r = 0.282) and sinuosity (Boac r = 0.505, Mogpog r = 0.257) were the highest for Mn in SW. This confirmed that the planform of the river affected the accumulation of Mn due to its effect on sediment deposition along the river and its potential to adsorb and/or desorb metals. Furthermore, the pH of SW also directly correlated with sediment Mn (r = 0.293), and inversely correlated with SW Mn (r = −0.465), which was expected as acidic water promotes the release of metals from sediments to SW. The results from this study will aid local government, environmental engineers and managers in their mitigation program through identification of the areas and segments in the river that contain the highest and the least contamination. This is to optimize financial and human resources during river system remediation and monitoring. Data and information extracted from this study are useful in other areas of similar condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".