Bioanalytical Monitoring of Laguna Lake (Philippines) to Assess Water Research Priorities Associated with Organic Chemical Pollution and Cyanobacterial Blooms
Bibliographic record
Abstract
Over several decades, anthropogenic influence has contributed to continuing water quality deterioration in Laguna Lake (Philippines), affecting the vital ecosystem services that it provides. To evaluate the potential ecotoxicological impacts of organic chemical pollution in the lake, a battery of in vitro bioassays (cytotoxicity, mutagenicity, and estrogenicity) was employed across 15 sites on Laguna Lake and a pond receiving secondary treated effluent that served as a positive control. Microcystin-LR concentrations were evaluated, given the historical occurrence of cyanobacterial blooms. Cytotoxicity (3.73 to 270 IC 10 -REF), mutagenicity (0.315 to 0.683 μg/L 4-NQO eq), and microcystin-LR (0.2 and 135 μg/L) were detected in lake samples suggesting potential toxicity risks. Cytotoxicity was correlated with microcystin-LR concentrations ( r = 0.84) though at this stage, it is difficult to directly associate cytotoxicity with cyanoblooms due to other potentially co-occurring substances that were not analyzed. Estrogenicity was not detected in the lake, despite previous studies suggesting the presence of estrogens. Overall, results indicate a need for a long-term monitoring plan that considers cyanobloom presence and effects-based monitoring. To improve reporting on the environmental state and communication among stakeholders, a water quality index was established. The index suggests that current conventional monitoring parameters may not accurately predict water quality, particularly during cyanoblooms.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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".