Quantifying the Relation Between Electrical Conductivity and Salt Concentration for Dilution Gauging Via Dry Salt Injection
Bibliographic record
Abstract
Salt dilution is a popular approach used for discharge measurement. This research focused on the procedure for determining the calibration factor (CFT) that is used to convert measured temperature corrected electrical conductivity to salt concentration for injection using dry salt. It is important to document the uncertainty in CFT because it contributes directly to uncertainty in the calculated discharge. Based on laboratory trials, it was found that the calibration should be performed as close to in situ stream temperature as possible to minimize errors. The discharge measurement and calibration procedure should be performed with the same probe to minimize uncertainty. Distilled water can be used instead of stream water for a calibration solution if an analytical correction is applied to account for differences in ionic composition of the water. The calibration factor can be determined with an uncertainty of less than ± 1% under “best-case” conditions, and the uncertainty may be as high as ± 4% under less favourable conditions. If calibration is not performed, CFT can be estimated from the relation between CFT and background temperature-corrected electrical conductivity (ECBG) with an uncertainty of about ± 2%, or estimated as a set value of 0.486 mg·cm·μS-1·L-1 with an uncertainty of about ± 2.8% for a properly calibrated probe. More testing should focus on streams with ECBG > 500 μS·cm-1, which were not well represented in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".