Monitoring Biological and Chemical Trends in Temperate Still Waters Using Citizen Science
Bibliographic record
Abstract
The involvement of volunteers in the monitoring of the environment holds great potential to gather information on a wider temporal and spatial scale than is currently possible. However, the mass involvement of citizens in monitoring freshwater health is a relatively new field and subject to uncertainty. Here, we examine 1192 samples collected across 46 temperate ponds (<2 ha) and 29 temperate lakes (>2 ha) by 120 volunteers trained through the FreshWater Watch citizen science programme to consider if the approach is able to (a) identify well established patterns in water quality and biological indicators (i.e., fish), and (b) provide a potentially useful basis for the identification of pollution sources in urban or peri-urban landscapes. Seasonal patterns observed agreed well with established principles of nutrient dynamics, algal bloom seasonality, and broad biological trends between ponds and lakes. Further, observational data collected by the volunteers suggested plausible links between the presence of residential discharge and water level fluctuation and significant increases in algal bloom observations between peri-urban and urban sites. We suggest that citizen science can have a role to play in complementing regulatory monitoring efforts and that local citizens should be empowered to become stewards of their local freshwater resources.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".