Sorption of steroid estrogens to soil and soil constituents in single- and multi-sorbate systems
Bibliographic record
Abstract
The sorptive behavior of 17 beta-estradiol (estradiol), estrone, and 17alpha-ethinylestradiol (EE2) from aqueous solutions to four soil samples, two clay minerals, and sand was examined. The measured sorption isotherms were found to be nonlinear and soil isotherm data fit the Freundlich model. Alternatively, both the Langmuir and Freundlich models were used for the mineral samples. The sorption affinity of steroid estrogens was found to be greater for montmorillonite than kaolinite and the sand. The soil Freundlich coefficients (K(F)) for estradiol, estrone, and EE2 were observed to increase with organic carbon (OC) content, and resulting Freundlich coefficients that were normalized to the OC content (K(F)OC) were observed to be within the same range for estradiol and estrone but not for EE2. Sorption of steroid estrogens in soil appears to be governed by OC and expanding clay mineral content; thus, estimating sorption coefficients from physicochemical properties may underestimate sorption in soils or sediments that are rich in OC and smectitic clay minerals. Analysis of soils by solid-state (13)C nuclear magnetic resonance did not reveal any trends between sorption capacity and organic matter structure. Competitive sorption experiments revealed that the degree of competition varied with the OC and mineral content, further suggesting that specific soil properties are important for understanding sorption of estrogens in terrestrial environments.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".