Locating and characterising groundwater storage areas within an alpine watershed using time‐lapse gravity, GPR and seismic refraction methods
Bibliographic record
Abstract
Abstract Unconsolidated sediments in alpine watersheds can store glacier melt and snowmelt as groundwater, which helps sustain flow in mountain rivers during dry periods. However, the amount and distribution of groundwater storage in rugged alpine terrain is not well understood, hindering our ability to predict the rate and timing of groundwater discharge into alpine streams. We show how non‐invasive time‐lapse microgravity surveys can be used to gauge the spatial distribution of groundwater storage changes within a large (ca 1500 × 1000 m) moraine–talus field of the Lake O'Hara alpine watershed of the Canadian Rockies. Additional ground‐penetrating radar (GPR) and seismic refraction surveys provide complementary information on subsurface bedrock topography and reveal the location of a major northwest–southeast trending depression that likely controls groundwater flow to an alpine lake contiguous with the moraine–talus field. Repeat relative gravity measurements made on a network of 80 gravity stations over and around the moraine–talus field during the summers of 2009 and 2010 reveal gravity changes of up to 25 µgal. Although the small gravity changes associated with groundwater flowing out of storage areas are noisy, significant changes are evident on the eastern side of the moraine–talus field. © Her Majesty the Queen in Right of Canada 2012.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".