Investigation of Hydrophobic Organic Carbon (HOC) Partitioning to 1 kDa Fractionated Municipal Wastewater Colloids
Bibliographic record
Abstract
Natural organic matter from the aquatic environment passing a 1 kDa filter has been hypothesized to not contribute appreciably to hydrophobic organic compound (HOC) partitioning; however, to our knowledge this limit has not been verified experimentally for any sorbate/sorbent system. Presently, colloidal organic carbon (COC) < 1 kDa approached 70% of the total COC (<1.5 μm) mass in primary effluent (PE) from a municipal wastewater treatment plant. Partitioning of HOCs 1,2,4,5-tetrachlorobenzene, pentachlorobenzene, and hexachlorobenzene to COC for both 1.5 μm and 1 kDa filtrates of PE was investigated using the gas-stripping technique. Contrary to the hypothesis, significant HOC-COC partitioning to the 1 kDa filtrate was observed with organic carbon-normalized partitioning coefficients (logKCOC) of 4.30, 4.36, and 3.74 for 1,2,4,5-TeCB, PeCB, and HCB, respectively. Further, partitioning to COC < 1 kDa dominated the overall partitioning of the three chlorobenzenes in the 1.5 μm filtrate, and the partitioning behavior did not follow the trend based on hydrophobicity (KOW). The results show that significant partitioning of HOC may occur to OC < 1 kDa and highlights the need for further experiments with other HOCs and COC characterization to better understand and explain the observed partitioning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".