Concentrations of Some Lipophilic Chemicals in Fresh Water May Be Underestimated by Conventional Dichloromethane Extraction
Bibliographic record
Abstract
Abstract A re-analysis of water samples collected to determine the occurrence of lipophilic organochlorine chemicals, total polychlorinated biphenyls (PCBs) and polynuclear aromatic hydrocarbons (PAHs) in six tributaries to Lake Ontario in 1997 to 1998 has shown relatively high concentrations of some of those chemicals in extracts of chromic acid-digested water after the water had been extracted at neutral pH. For organochlorine chemicals and PCBs the effect was dramatic—for some tributaries the sums of concentrations of the chemicals in the acidic extracts over all sampling dates were larger than the sums of concentrations of the chemicals in the (neutral) water plus suspended solids fraction that had been determined previously. In addition, some chemicals were found in the acidic extracts that were not found in the prior extracts of (neutral) water plus suspended solids. Although some individual PAHs were found at relatively high concentrations in the acidic extracts compared to extracts of (neutral) water plus suspended solids, in general the phenomenon was not significant for total PAHs. The implication of our finding is that conventional dichloromethane extraction of neutral (filtered) water and the suspended solids phases can significantly underestimate the concentrations of some lipophilic chemicals such as chlorinated hydrocarbons and PCBs in fresh water, leading to an underestimation of their loadings to aquatic ecosystems. However, it should be noted that the biological availability of chemicals that are only extractable after rigorous extraction of the water may be doubtful.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".