A Preliminary Study of the Quality of Seawater at Rasfannu Beach of Male’ City
Bibliographic record
Abstract
The coastal waters of Malé, Maldives, suffer from pollution due to sewage anduntreated waste effluent discharge. Unlike other islands, Malé lacks natural beaches, leading tothe creation of two artificial beaches. These beaches are overcrowded, and concerns about waterquality persist due to poor water circulation and nearby sewage pipes. This study aims to assessthe water quality of Rasfannu, a recently created artificial beach. Water samples were collectedweekly over four weeks and analyzed for physicochemical parameters (pH, turbidity,conductivity, nitrate, nitrite, nitrogen ammonia) and bacteriological parameters (E. coli, fecalcoliform, total coliform). The membrane filtration method was used for bacterial analysis, whileabsorption spectroscopy was used for measuring nitrate, nitrite, and nitrogen ammonia. The pHand conductivity were measured using the Mettler Toledo pH meter. The results were comparedwith guidelines from the World Health Organization (WHO), United States EnvironmentalProtection Agency (USEPA), recreational water guidelines from Canada, California, and theEuropean Union (EU). The hypothesis was that the water at Rasfannu beach is contaminated andunsafe for recreational purposes. However, the results indicated that all parameters fell withinacceptable ranges as per these guidelines, and the water quality index calculated following theNational Sanitation Foundation (NSF) rated the water quality as good. Thus, Rasfannu beach isdeemed safe for recreational use.
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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 | 0.001 | 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) | 0.001 | 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".