Modeling Diffuse Nutrient and Sediment Pollution affecting Lake Palakpakin, Laguna using QSWAT
Bibliographic record
Abstract
Lake Palakpakin is vulnerable to nutrient and sediment pollution, leading to eutrophication and siltation.Despite existing studies identifying the pollutants and detecting their concentration within the lake, their sources were generally attributed to agricultural activities, considered a nonpoint source.This research modelled the diffuse sediment and nutrient pollution affecting Lake Palakpakin, San Pablo City, Laguna using Soil and Water Analysis Tool (SWAT).This model is physically based, requiring DEM, soil map, LULC map, slope map, and meteorological data as inputs to model physical processes associated with water movement, sediment movement, nutrient cycling, etc. Due to the unsatisfactory statistical results of the calibrated model caused by insufficient hydrological data, the uncalibrated simulation was used.It revealed that the critical source areas for NO3-N and PO4 are found in agricultural lands and areas that are underlain by clay and clay loam.Critical source areas were also found along the stream connecting Palakpakin lake to Laguna de Bay.Meanwhile, higher sediment yields were distinct around Sampaloc Lake and the outlet of the other lakes and in agricultural and urban areas.Moreover, the model exhibited that an increase in precipitation coincides with an increase in sediment, NO3-N and PO4 loading.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".