Tidal And Seasonal Effects on Water Quality in the Matang Mangrove Forest Reserve, Malaysia
Bibliographic record
Abstract
The Matang Mangrove Forest Reserve (MMFR) in Malaysia, known for its sustainable management, However, the specific relationships between tidal dynamics, seasonal changes, and water quality parameters within MMFR remain understudied. This study investigates the effects of tidal and seasonal fluctuations on water quality by examining seven parameters—Dissolved Oxygen, Salinity, Temperature, Total Dissolved Solids, pH, Turbidity, and Electric Conductivity—alongside river characteristics such as width, depth, and velocity. In-situ measurements were conducted across dry and wet seasons at both high and low tides to capture variability in water quality. The findings indicate that tidal cycles and seasonal changes significantly influence the parameters studied, with distinct patterns observed in relation to tidal conditions. For instance, salinity and turbidity levels were found to increase during high tide, influenced by seawater intrusion, while dissolved oxygen and temperature varied with seasonal rainfall and evaporation. These fluctuations not only reflect the hydrological processes within MMFR but also highlight the sensitivity of water quality to environmental conditions. Understanding these relationships is essential for developing adaptive management strategies that address the challenges posed by climate change and human impacts.
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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) | 0.000 | 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) | 0.000 | 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".