Recharge mixing in a complex distributary spring system in the Missouri Ozarks, USA
Bibliographic record
Abstract
Toronto Springs is a complex distributary karst spring system with 11 perennial springs in the Missouri Ozarks, USA. Carroll Cave (CC) and Wet Glaize Creek (WG) were previously identified as principal recharge sources. This study (1) characterized physical and chemical properties of springs and recharge sources; (2) developed end-member mixing models to estimate contributing proportions of CC and WG; and (3) created a conceptual model for the system. Samples analyzed for major ions and specific conductivity, in conjunction with a rotating continuous monitoring program to identify statistically comparable baseflow conditions, were used to assess differences among the sites. Monitoring data showed that the springs differed depending upon recharge proportions. Cluster analysis of average ion concentrations supported the choice of CC and WG as mixing model end members. Results showed a range in the proportions of the recharge sources, from surface-water to groundwater dominated. A conceptual model suggests that a system of distinct conduits beneath the WG flood plain transmits water to the individual springs. These conduits controlled the end-member recharge contributions and water chemistry of the springs. Interpretation of relative proportions of recharge contributions extends existing knowledge of karst hydrologic geometry beyond that of point-to-point connections to revealing complex surface-water/groundwater mixing in heterogeneous distributary spring 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".