Influence of Acidification on the Partitioning of Steroid Hormones among Filtrate, Filter Media, and Retained Particulate Matter
Bibliographic record
Abstract
Hormone contamination of aquatic systems has been shown to have deleterious effects on aquatic biota. However, the assessment of hormone contamination of aquatic environments requires a quantitative evaluation of the potential effects of sample preservation on hormone concentrations. This study investigated the influence of acidification (pH 2) of surface water samples on the partitioning of hormones among filtrate, filter media, and filter‐retained particulate matter. Hormones were spiked into unpreserved and sulfuric acid–preserved ultrapure water and surface water runoff samples. The samples were filtered, and hormones were extracted from the filter and filtrate and analyzed by high‐performance liquid chromatography. Acidification did not influence the partitioning of hormones onto the filter media. For the majority of the hormones investigated in this study, the partitioning of hormones to the filter‐retained particulate matter was not influenced by acidification. Acidification increased the partitioning of progesterone and melengestrol acetate onto the retained particulate matter (about 25% for both analytes). Incorporation of an isotopically labeled internal standard (ISTD) for progesterone accounted for the loss of progesterone to the filter‐retained particulates and resulted in accurate concentrations of progesterone in the filtrate. The incorporation of an ISTD for melengestrol acetate, however, was unable to account for the loss of melengestrol acetate to the retained particulates and resulted in underestimations of melengestrol acetate in the filtrate. Our results indicate that the analysis of melengestrol acetate in acid preserved surface runoff samples should be conducted on the filter‐retained particulates as well as the filtrate. Core Ideas Acidification did not influence the partitioning of hormones onto the filter media. Acidification increased partitioning of progesterone to particulates. Acidification increased partitioning of melengestrol acetate to particulates. Internal standard accounted for partitioning of progesterone to particulates. Internal standard did not account for partitioning of melengestrol acetate to particulates.
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 | 0.001 |
| 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 | 0.001 | 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".