Hydrogeological characterization of alpine karst using the transient analysis of flow and transport
Bibliographic record
Abstract
Abstract. Karst springs in alpine catchments are important for maintaining groundwater-dependent ecosystems in fragile environments and for sustaining baseflow in mountain rivers. Despite its importance, rugged and inaccessible terrains pose major challenges in hydrogeological studies of alpine karst. This study developed a practical approach for characterizing an alpine karst system in the Canadian Rocky Mountains that had no previous information aside from the location of the spring outlet. Using geological maps, satellite images, simple water balance, water sampling and analysis, and dye tracer tests, it was possible to estimate the extent of the spring catchment and infer the hydrogeological characteristics of the karst system. Of particular importance was the information obtained from the fluctuations of spring discharge and electrical conductivity in response to diurnal snowmelt cycles. Synthesis of the diverse data set indicates that the karst system has a large volume of groundwater stored in the fractured rock matrix that buffers the interannual variability of precipitation and sustains steady baseflow throughout the year. The karst system consists of fractured rock matrix, saturated conduits acting like pipes, unsaturated conduits acting like open channels, and many pools delaying the propagation of transport and hydraulic signals through the conduit network. The approach developed in this study will be applicable to other alpine karst systems in snow-dominated catchments in rugged and inaccessible terrains.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".