Unseen and overlooked: methods for quantifying groundwater abstraction from different sectors in a data-scarce region, British Columbia, Canada
Bibliographic record
Abstract
Groundwater is considered a reliable resource, relatively insensitive to seasonal or even multi-year climatic variation; however, quantifying aquifer-scale estimates of stress in diverse hydrologic environments is particularly difficult due to data scarcity and the limited methods for deriving stress parameters, such as groundwater use and availability, which can be applied over a large spatial area. On a global scale, most methods focus on one major sector, such as irrigated agriculture which accounts for a substantial portion of groundwater use on a global-scale. However, this may misrepresent groundwater abstractions in regions significantly impacted by other sectors on a local-scale. The objective of this paper is to quantify annual average groundwater use through a multi-method sectoral approach for regions where groundwater abstraction data are scarce. Sectoral methods are developed for the annual volumetric quantification and spatial distribution of groundwater use for municipal water distribution systems, private domestic well users in municipal and rural regions, industrial use for manufacturing, mining, and oil and gas industries, irrigated agriculture, and finfish aquaculture. Results suggest that British Columbia (BC) uses a total of ∼562 million cubic metres of groundwater annually. The largest annual groundwater use by major sector is agriculture (38%), finfish aquaculture (21%), industrial (16%), municipal water distribution systems (15%), and domestic private well users (11%). This paper highlights the implications of using downscaled values of groundwater use from global datasets for aquifer-scale estimates, which can be misrepresentative in regions where groundwater use is unregulated or newly regulated, as is the case in BC. The sectoral methods developed in this paper provide a framework for estimating groundwater-specific use estimates in data scarce regions critical for groundwater management plans and aquifer-scale groundwater stress studies which depend on spatially-distributed groundwater use data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".