Using citizen science to understand river water quality while filling data gaps to meet United Nations Sustainable Development Goal 6 objectives
Bibliographic record
Abstract
This study investigates water quality along the river Liffey in Dublin city with the help of citizen scientists, including the community of river users such as paddle boarders and those accessing the river from the bank. The primary objective was to evaluate water quality near sources of pollution observed by citizens, while filling data gaps for the United Nations (UN) Sustainable Development Goal (SDG) 6, Indicator 6.3.2. The participants used field chemistry kits to measure nitrate (NO₃-N) and phosphate (PO₄-P) at 19 locations on a monthly basis over the course of nine months, recording the results on a smartphone app. 10% of nitrate samples were indicative of low quality water values while 35.6% of phosphate samples were indicative of low quality water. Rainfall over the study period was analysed to investigate the impact of run-off from rainwater on the river. Results indicated that excessive rainfall was not a factor in lower water quality in this area. Citizen scientists' observational notes and photographs entered onto the database, with accompanying test results were key to highlighting pollution sources at specific locations which correlated with high levels of nitrate and phosphate resulting in low quality water. Land use was a factor in these areas of recent housing development indicating possible domestic misconnections. Citizen scientist data has the potential to fulfil UN SDG 6, in contributing to Indicator 6.3.2 while detecting contamination.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".