Early Influence of the COVID‐19 Pandemic on Volunteer Water Monitoring Programs in the United States and Canada
Bibliographic record
Abstract
Volunteer water monitoring programs generate new scientific knowledge, contribute data to decision-making processes, and increase social networks, technical knowledge, and skills of participants. Declaration of the COVID-19 pandemic threatened the ability of these programs to continue to engage volunteers to achieve such outcomes. A national water monitoring network hosted a brainstorming webinar to facilitate communication across programs to identify potential solutions to pandemic-influenced challenges. Following that webinar, a survey of United States and Canadian volunteer monitoring programs that was conducted about 3 months into the pandemic revealed that 72% of 80 responding programs planned to carry on through the 2020 field season despite most having experienced delayed starts. Other common program modifications implemented in the first months of the pandemic included adding COVID-19 safety information to program guidance, changing field team composition, monitoring timing and logistics, and adopting new communications strategies. Most programs reported loss or anticipated loss in number of data observations (74%) and volunteers (66%), while 44% reported known or anticipated losses in funding. Seventeen percent of responding programs were able to swiftly develop distance learning tools to train participants, which led to increased program capacity to reach broader audiences.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".