Correlating specific conductivity with total hardness in gypsum karst waters
Bibliographic record
Abstract
Abstract Modern digital conductivity meters are readily portable, robust, cheap, and give precisely reproducible values of specific electrical conductivity (SpC, in µS cm−1). Here we investigate the accuracy of their estimates of the amounts of gypsum dissolved in waters collected in gypsum karst terrains, expressed as total hardness (TH) in mg L−1 of CaSO4·2H2O (GYP). Total dissolved solid concentrations (TDS) are also considered. Curves obtained with the program PHREEQC, for the dissolution of pure gypsum in water at 25 C, are compared with 574 comprehensive water chemical analyses selected from gypsum karst studies in Europe and the Americas. Principal common and foreign ions encountered are the BNC group (bicarbonates, nitrates, chlorides). It is found that GYP = 1·12·SpC + 62 where BNC < 33% (Cl− < 5%), with one standard error <5% for waters with SpC > 2400 µS cm−1; GYP = 0·74·SpC + 777 where BNC < 33% (5% ≤ Cl− < 15%), with one standard error <10% for waters with SpC > 3100 µS cm−1; GYP = 0·97·SpC − 209 where BNC < 33% and Cl− ≥ 15%, with one standard error <10% for samples with SpC > 4300 µS cm−1. There are similar results for the more complex waters found in gypsum karsts where much carbonate rock or salt is also present, to the limit of BNC < 50% for what may reasonably be defined as ‘gypsum waters’. Values of R2 for linear correlations of different subsets of the water samples range from 0·69 to 0·96, the majority being >0·8. Copyright © 2006 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 | 0.001 |
| 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".