Tracer tests in karst hydrogeology and speleology
Bibliographic record
Abstract
This article presents an introduction to the fundamentals of tracing techniques and their application in cave and karst environments, illustrated by case studies from the Mammoth Cave, USA, and a small experimental site in Switzerland. The properties and limitations of the most important artificial tracers are discussed, and the available methods of tracer injection, sampling, online monitoring and laboratory analysis are presented. Fully quantitative tracer experiments result in continuous or discrete concentration-time data series, i.e. breakthrough curves, and concomitant discharge data, which make it possible to obtain detailed information about groundwater flow and contaminant transport. Within the frame of speleological investigations, tracer tests can help to resolve the active and often inaccessible part of cave and conduit networks and to obtain indications about the geometry and volume of the conduits. For hydrogeological studies, caves can in turn be used as natural experimental and monitoring sites inside the unsaturated or saturated zone of karst aquifer systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".