Citizen-science approach to long-term observation of aquifer response to meteorological forcings
Bibliographic record
Abstract
Groundwater provides more stable source of water supply, compared to surface water, in the areas that experience large fluctuations in dry/wet condition, such as semi-arid, continental regions of Africa, Asia, and Americas. These regions are characterized by decadal-scale variability in precipitation, which translates into variability in groundwater recharge rates. Depending on the balance between precipitation and evapotranspiration, these regions can have several consecutive years of no or little recharge, causing a substantial decline of aquifer storage. Therefore, it is important to have a dense network of observation wells to monitor the spatial and temporal variability in aquifer storage over a long term, particularly in regions with highly heterogeneous aquifers. Groundwater in a system of heterogeneous aquifers is disintegrated, meaning that a large number of wells are required to monitor the behavior of many individual aquifer units. This is in contrast to the surface water system integrated by the river network, which can be effectively monitored using a relatively small number of river-gauging stations. As an example, the Paskapoo Formation aquifer system in Alberta, Canada, consists of numerous small sandstone aquifer units encased in mudstone aquitards, resulting in highly heterogenous behavior of aquifer water levels over a distance of < 1000 m. A community-based groundwater monitoring network was initiated in the Rocky View County in 2007-2008 to monitor the response of the Paskapoo Formation aquifer to multi-decadal fluctuations in meteorological forcings. The county occupies ~4000 km2 of predominantly agricultural area under the semi-arid climate, where mean annual precipitation is 400-500 mm and annual potential evapotranspiration is 600-700 mm. A citizen-science approach is used, whereby community volunteers monitor their own wells and report their observation to university researchers. The groundwater monitoring network is part of a hydrological observatory including stream gauging stations and meteorological stations measuring precipitation and evapotranspiration fluxes. The long-term data collected at the observatory are used to demonstrate the effects of wet-dry cycles on soil moisture, groundwater storage, and stream flow; and to predict responses of aquifer storage to climate-change scenarios using numerical groundwater recharge models.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".