Limnological applications of the Thermodynamic Equation of Seawater 2010 (TEOS‐10)
Bibliographic record
Abstract
The new Thermodynamic Equation of Seawater 2010 (TEOS‐10) provides a highly accurate, thermodynamically consistent, and complete representation of all thermodynamic properties of seawater over a wide range in temperature and salinity. These properties include density, sound speed, heat capacity, enthalpy, chemical potential, and many others. Here we provide simple procedures by which limnological studies can take advantage of TEOS‐10. These require replacing the TEOS‐10 salinity argument, seawater Absolute Salinity SA, with a limnological salinity Sa. In typical natural waters where anion concentrations are dominated by HCO3− or Cl−, Sa can be approximated by the solution salinity Sasoln, obtained by summing the concentrations of different dissolved constituents in a complete chemical analysis after multiplying by their molar masses. Slightly better results can be obtained, especially at very low salinities where silicic acid Si(OH)4 is an important constituent, by using a density salinity Sadens carefully defined by dividing the solution salinity into an ionic and a nonionic part and changing the weighting factors for each in the summation. In cases where the dominant anion is HCO3−, which covers the majority of inland waters, measurements of electrical conductivity can be used to estimate the ionic salinity, which will be ≈60% greater than the Absolute Salinity of seawater of the same conductivity. The resulting estimates of that part of density in excess of pure water density at the same temperature will be accurate within ± 10%.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".