A Comprehensive and Critical Compilation, Evaluation, and Selection of Physical–Chemical Property Data for Selected Polychlorinated Biphenyls
Bibliographic record
Abstract
Accurate physical–chemical properties (aqueous solubility SW, octanol–water partition coefficient KOW, vapor pressure P, Henry’s law constant H, octanol–air partition coefficient KOA, octanol solubility SO) are of fundamental importance for modeling the transport and fate of organic pollutants in the environment. Energies of phase transfer are used to describe the temperature dependence of these properties. When trying to quantify the behavior of contaminant mixtures such as the polychlorinated biphenyls, consistent physical–chemical properties are required for each individual congener. A complete set of temperature dependent property data for sixteen polychlorinated biphenyls (PCB-3, 8, 15, 28, 29, 31, 52, 61, 101, 105, 118, 138, 153, 155, 180, 194) was derived, based on all experimentally obtained values reported for these congeners in the literature. Log mean values derived from the experimental data were adjusted to yield an internally consistent set of data for each congener. These adjusted data also show a greater degree of interhomologue consistency, which can be illustrated with the help of simple quantitative structure-property relationships that use molar mass and the number of chlorine substitutions in ortho-positions as descriptors. The extent of the required adjustment gives an indication of the uncertainty of the averaged measured values and is typically lower than might be expected from the range of the reported measured values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".