Ecological indicators of water quality and marshland impact area (MARia) index of Ligawasan Marsh: a critically important wetland in the Southern Mindanao, Philippines
Bibliographic record
Abstract
Abstract Wetland ecosystems are vital for both biodiversity and communities that depend on them. The Ligawasan Marsh in the southern part of the Philippines is one of the most threatened wetlands in the country. Apart from increasing anthropogenic development, wetlands have faced warfare-driven threats that have hindered research and conservation efforts in the area for many decades. Our study is the first to investigate the patterns of physicochemical parameters, patterns of land use cover, and the relationship between land use change and marshland environmental status. We also developed the Marshland Impact Area (MARia) index to assess the potential impact contributions of the surrounding towns. We found a strong negative correlation between dissolved oxygen (DO) and indicators of pollution such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and heavy metals. Interestingly, we observed elevated levels of phosphate and mercury at all sampling sites in the Ligawasan Marsh. Our landscape-level modelling showed that these elevated levels are associated with expanding croplands and urbanisation surrounding the marsh. We found that the potential impact contribution of cropland and urbanisation from surrounding towns of the Ligawasan Marsh varied significantly, suggesting the importance of implementing local policies to reduce land use change impacts. With the current yet remaims limited knowledge and beyond safe pollution levels in the Ligawasan Marsh, it is crucial to implement collaborative and science-based governance to integrate conservation initiatives with the priorities of global targets, such as the Kunming-Montreal Global Biodiversity Framework, for a comprehensive and sustainable approach to Ligawasan Marsh conservation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".