Karst Conduit Networks, Connectivity and Recharge Dynamics of a Sinkhole
Bibliographic record
Abstract
Management of karst aquifers is often limited by a lack of understanding of recharge and flow dynamics. This article presents the identification of conduit networks and the connectivity, dynamic nature of recharge and inherent uncertainties in recharge assessment in karstic settings. The study was carried out at two large sinkholes located in the Poocher Swamp fresh water lens, south east of South Australia. Point recharge to the sinkholes was calculated using stream flow data at gauging stations and water balance of the swamp. Conduit system and their interconnectivity in the vicinity of sinkholes were characterized by the use of transient electromagnetic survey (TEM) to identify high potential porosity zones of the aquifer. Resistivity data were used to estimate aquifer porosities using Archie’s law. Recharge response to the karstic aquifer was monitored using four monitoring wells located at various distances from sinkholes. Measurements were taken during recharge and recession phases. Four dynamic stages of water level rise and fall were observed in response to filling of conduit zones, transmission and possible effects of entrapped air pressure within conduits. Electrical conductivity (EC) profiles were obtained at two stages. These confirmed interconnectivity of conduits, and re-adjustment to ambient groundwater quality following the recharge event. The lower EC water was found in the monitoring well furthest from the sinkholes indicating the complexity of conduit connection and the nature of mixing with ambient groundwater.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".