Coastal Sediment Transport and Environmental Management Analysis of the Dredging Activity of Maloma River in San Felipe, Zambales, Philippines
Bibliographic record
Abstract
Maloma River is one of the three major rivers of Zambales that has been clogged by lahar since the eruption of Mt.Pinatubo.To mitigate the risks of flooding, the government has embarked on a rehabilitation of the river through a 60-hectare dredging project.However, the project needed more support from the local populace due to the potential environmental and social (E&S) risks of dredging that they have previously seen in the dredging of the Bucao River, another of the three major river basins.To address the concerns regarding potential coastal erosion, the researchers investigated the sediment transport for the assessment of coastal erosion of the Maloma River Rehabilitation Project.The study aimed to comprehensively analyze the dredging-influenced sediment dynamics of the Maloma River using Delft3D and recommend effective management strategies.To supplement the data from the EIA of the project, third-party data were sourced from corresponding institutions for oceanographic, meteorological, and sediment characteristics.In Delft3D-FLOW, one of the modules of Delft3D, the initial conditions used were applied to run simulations with a baseline bathymetry and another with a dredged bathymetry generated via ArcGIS.Among the six (6) observation points, sediment activities were apparent in the Maloma Bridge.The peak of cumulative erosion/sedimentation (m) from the baseline simulation coincided with the Habagat season.While for the dredged simulation, the cum.erosion/sedimentation sky rocketed suggesting Maloma Bridge was a dredginginduced hazard area.However, it must be noted that for both simulations, a persistent error of water level that was too high was prompted by the predetermined limitations of the study.As such, the researchers recommend further research including project-specific surveys to aid the model's accuracy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".