Limitations in the use of electrical conductivity to monitor the behaviour of soil solution
Bibliographic record
Abstract
Solutions of KBr and K2SO4 of various concentrations were separately displaced by deionised water through 2 contrasting saturated materials, inert solid particles (glass ballotini), and a reactive but non-swelling aggregated clay mineral (sepiolite) over a wide range of flow rates. The concentration of the individual ions in the effluent was analysed (Br– and K+ with ion-specific electrodes, SO42+ by ion chromatography) and that of bulk solution was measured by electrical conductivity (EC). For each displacement, the individual breakthrough curves (BTCs) for the anion, the cation, and the bulk solution were optimised by CXTFIT 2.0. In ballotini, the BTCs of the anion, cation, and solution were always congruent, the retardation factors did not differ significantly from unity, and the coefficients of hydrodynamic dispersion were identical. For sepiolite, the ions were separated; the bulk solution eluted faster than the cation, slower than the anions. Retardation factors were always less than unity for the anions, greater than unity for the cation, and close to but less than unity for the bulk solution, and became more extreme as the concentration of solute decreased. Dispersion coefficients were, however, unaffected by type of solute, concentration range, or particular ion/EC. The separation of ions means that the composition as well as the concentration of a solution changes continuously during flow through a reactive soil. Estimates of solution concentration from measurements of EC may thus fail to characterise adequately the movement of the individual components of the solution in such materials. This has implications for the interpretation of any leachate monitoring in reactive soils by methods based on the measurement of EC, such as time-domain reflectometry.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".