On the Relationship between <i>D</i><sub>o</sub><sub>w</sub> and <i>K</i><sub>ow</sub> in Natural Waters
Bibliographic record
Abstract
The relationship between the overall octanol-water partition coefficient of a mixture of related chemical species, D(ow), and the octanol-water partition coefficients of its components, (K(ow))i, is explored. One form of the relationship (model 1) is generally applicable but relies on definition of aqueous phase speciation at equilibrium with octanol. An alternative form of the relationship (model 2) circumvents this requirement but assumes that related species are conserved during the partitioning process and is explicitly dependent on the water to octanol volume ratio, Vw/Vo. The potential applications and limitations of each model for defining the hydrophobic characteristics of chemical species in natural waters are examined in the light of experimental partition results for dissolved Cu and Pb in river waters. Given the general difficulties in accurate speciation modeling of trace metals in natural samples, model 1 was only able to estimate a K(ow) (typically in the range 0.03-0.3) for a computed organically complexed fraction of metal (generally > 90%). However, by conducting partition "isotherms" as a function of Vw/Vo and, because of the buffering capacity of natural waters, by treating a sample as two distinct hydrophilic and hydrophobic "pools", model 2 was able to estimate both the abundance and K(ow) of a more specific group of species. Parameter values derived from the latter approach indicated that river waters comprise a relatively small pool (about 4-20%) of metal whose octanol-water partitioning is in the region of 15-150. Given that the free ion activity of strongly binding metals in natural waters is extremely small, the hydrophobic fraction may, in many cases, representthe most biologically and environmentally significant component of metal. Accordingly, the experimental and modeling approaches described herein could be of great significance to an improved understanding of the fate and impacts of trace metals in the aquatic environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".