Numerical modelling of the impacts of water abstraction for hydraulic fracturing on groundwater–surface water interaction: a case study from northwestern Alberta, Canada
Bibliographic record
Abstract
The temporal dynamics of groundwater–surface water interaction under the impacts of various water abstraction scenarios are presented for hydraulic fracturing in a shale gas and oil play area (23 984.9 km2), Alberta, Canada, using the MIKE-SHE and MIKE-11 models. Water-use data for hydraulic fracturing were obtained for 433 wells drilled in the study area in 2013 and 2014. Modelling results indicate that water abstraction for hydraulic fracturing has very small (<0.35%) negative impacts on mean monthly and annual river and groundwater levels and stream and groundwater flows in the study area, and small (1–4.17%) negative impacts on environmental flows near the water abstraction location during low-flow periods. The impacts on environmental flow depend on the amount of water abstraction and the daily flow over time at a specific river cross-section. The results also indicate a very small (<0.35%) positive impact on mean monthly and annual groundwater contributions to streamflow because of the large study area. The results provide useful information for planning long-term seasonal and annual water abstractions from the river and groundwater for hydraulic fracturing in a large study area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".