Community science to the rescue: capturing water quality data during the COVID-19 pandemic
Bibliographic record
Abstract
In 2020, the COVID-19 pandemic interrupted all aspects of human activity, including environmental research and monitoring. Despite a lack of laboratory access and other restrictive measures, we adapted an existing community science monitoring program to continue through the summer of 2020. We worked with local community groups to recruit 58 volunteers who collected lake water samples from 60 sites on 16 lakes in south-central Ontario from June to September 2020. We organized drop-off depots and had volunteers freeze samples to monitor nearshore nutrients (phosphorus and nitrogen) and chlorophyll-a. A survey was distributed to volunteers to analyze lake-front property owners’ activities during the pandemic. We found spatial patterns in nearshore water quality across the lakes, with sub-watershed development being a significant predictor of nutrients and chlorophyll-a. Additionally, pre-pandemic (2019) and pandemic (2020 and 2021) nutrient concentrations were compared, but there was no clear impact of the pandemic on nearshore nutrient concentrations, despite changes in lake-front property owners activities. Overall, this study demonstrated the ability of community science to provide water quality data on a large spatial scale despite a major societal disruption, providing insight into regional nutrient trends during the first year of the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".