Bibliographic record
Abstract
An algorithm is developed to compute the conductivity of lake and dilute ocean water from measured chemical composition at arbitrary temperature and pressure. The complex mixed electrolyte is considered as a sum of binary electrolytes rather than a sum of ions. Effects of ion association are included, and it is found that pairing effects are important in natural freshwaters. Bounds on the accuracy of the algorithm for specific classes of binary electrolytes are assessed and it is estimated that the algorithm has an overall accuracy of better than 2% for salinities less than about 4 g L−1. Comparison with seawater conductivities is much better than 1%, but predicted conductivities of some published analyses of river waters are about 3% too high. Some of this difference may be due to a lack of data on ion pairing effects between bivalent metals and bicarbonate, but also may result from uncertainties in the measured chemical composition and measured conductivity. An iterative procedure incorporating this algorithm is used to compute reference conductivity at 25°C and salinity from in situ measurements of conductivity in waters where only relative amounts of ions are known. It is found that the conversion to reference conductivity is reasonably independent (to within about 1%) of the ionic composition for most world river waters, but is somewhat different than that for KCl solutions. However, derived salinities are quite sensitive to the composition, and the ratio of ionic salinity to reference conductivity varies between 0.6 and 0.9 mg L−1 (µS cm−1)−1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".