Characterizing groundwater flow paths in an undeveloped region through synoptic river sampling for environmental tracers
Bibliographic record
Abstract
Abstract Synoptic sampling of three rivers for a suite of environmental tracers is shown to be an efficient way to gain an understanding of groundwater flow paths for a previously unstudied large area in Alberta, Canada. For regional‐scale characterization, classical hydrogeological techniques are limited by the location and number of groundwater wells. This study demonstrates that rivers can become an easily accessible location to sample the distribution of groundwater flow paths discharging to surface water. Modelling of groundwater discharge to the rivers and groundwater mean age helps generate knowledge of groundwater circulation for a large area, which is useful for conceptual model development and focusing future characterization efforts. Results indicate that the benchland areas in this region, with higher topographic relief, had hydrogeological conditions that favoured deeper groundwater circulation with a modelled mean age greater than 100 years from recharge to discharge. Lower relief areas, which coincide with a transition in bedrock formations in this region, appeared to have much shorter and shallower groundwater circulation. The approach required a field program completed in 5 days and financial budget approximately equivalent to drilling a single borehole and installing a monitoring well. It is concluded that under the right conditions, where few classical observation points exist and knowledge is limited, synoptic sampling of rivers can be used to develop scientifically defensible conceptual models at a comparable scale to regional planning and resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".