Correlating specific conductivity with total hardness in limestone and dolomite karst waters
Bibliographic record
Abstract
Abstract Under field conditions modern digital conductivity meters give standardized, rapid and reproducible measurements. Here we investigate the accuracy of their estimates of the composition of karst waters, as total hardness (TH, as mg/L CaCO3) for limestone and dolomite. These are the fundamental measures of process in carbonate karst geomorphology. PHREEQC theoretical curves for the dissolution of pure calcite/aragonite and dolomite in water at 25 °C are compared with water analyses from karst studies worldwide. Other principal ions encountered are sulphates, nitrates and chlorides (the ‘SNC’ group). From carbonate karsts, 2309 spring, well and stream samples were divided into uncontaminated (SNC < 10%), moderately contaminated (10 < SNC < 20%), and contaminated (SNC > 20%) classes. Where specific conductivity (SpC) is less than 600 µS/cm, a clear statistical distinction can be drawn between waters having little contamination and substantially contaminated waters with SNC > 20%. As sometimes claimed in manufacturers' literature, in ‘clean’ limestone waters TH is close to 1/2SpC, with a standard error of 2–3 mg/L. The slope of the best‐fit line for 1949 samples covering all SNC classes where SpC < 600 µS/cm is 1·86, very close to the 1·88 obtained for clean limestone waters; however, the value of the intercept is ten times higher. The regression line for clean limestone waters where SpC > 600 µS/cm helps to distinguish polluted waters from clean waters with possible endogenic sources of CO2. In the range 250 < SpC < 600 µS/cm, dolomite waters can be readily distinguished from limestone waters. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".