Community‐Based Groundwater Monitoring Network Using a Citizen‐Science Approach
Bibliographic record
Abstract
Water level monitoring provides essential information about the condition of aquifers and their responses to water extraction, land-use change, and climatic variability. It is important to have a spatially distributed, long-term monitoring well network for sustainable groundwater resource management. Community-based monitoring involving citizen scientists provides an approach to complement existing government-run monitoring programs. This article demonstrates the feasibility of establishing a large-scale water level monitoring network of private water supply wells using an example from Rocky View County (3900 km(2) ) in Alberta, Canada. In this network, community volunteers measure the water level in their wells, and enter these data through a web-based data portal, which allows the public to view and download these data. The close collaboration among the university researchers, county staff members, and community volunteers enabled the successful implementation and operation of the network for a 5-year pilot period, which generated valuable data sets. The monitoring program was accompanied by education and outreach programs, in which the educational materials on groundwater were developed in collaboration with science teachers from local schools. The methodology used in this study can be easily adopted by other municipalities and watershed stewardship groups interested in groundwater monitoring. As governments are starting to rely increasingly on local municipalities and conservation authorities for watershed management and planning, community-based groundwater monitoring provides an effective and affordable tool for sustainable water resources management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".