iWetland: A Community Science Platform for Monitoring Wetland Water Levels
Bibliographic record
Abstract
iWetland is a community science wetland water level monitoring platform developed by the McMaster Ecohydrology Lab and tested from 2016 to 2019 in wetlands located east of Georgian Bay, Ontario, Canada. The goal of iWetland is to engage community members in wetland science while collecting data to better understand the spatiotemporal variability in water level patterns of wetlands. We installed 24 iWetland water level monitoring stations in popular hiking and camping areas where visitors can text the water level of the wetland to an online database that automatically collates the data. Here, we share our approach for developing the iWetland community science platform and its importance for monitoring all types of wetland ecosystems. From 2016 through 2019, almost 2,000 individuals recorded more than 2,600 water table measurements. The iWetland platform successfully collected accurate water table data for 24 wetlands. We discuss the successes and shortcomings of the community science platform with respect to data collection, community engagement, and participation. We found that forming mutually beneficial partnerships with community groups paired with strong outreach presence were key to the success of this community science platform. Finally, we recommend that those interested in adopting the iWetland platform in their community partner with community groups, recognize participant contributions, identify accessible sites, and host outreach activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".