Estrogens: Properties, behaviors, and fate in dairy manure-amended soils
Bibliographic record
Abstract
In 2012 there were 63% fewer dairies in the United States than there were in 1997 as a result of conglomeration of the dairy industry into concentrated animal feeding operations at the expense of smaller farms. Today, 60% of all milk produced in the United States comes from 5% of the nation’s dairies (operations with ≥ 500 cows). Concentrated animal feeding operations are touted as economically efficient agricultural business models, hailed for their increased milk yields. Yet, with an average daily manure production of over 27 000 kg for a 500-head dairy farm, manure storage and disposal are serious management and environmental concerns. A common economical mode of manure disposal is application to nearby agricultural fields. However, a major concern with land application of dairy manure is the fate of manure-borne hormones, compounds considered chemicals of emerging concern, and the potential threat these hormones pose to humans and the environment. The fate of these chemicals in the soil environment is complicated by multiple edaphic variables including pH, mineralogy, organic matter, microbial activity, and redox status. Estrogens are sorbed by soil organic matter and transformed to nonbioactive, highly soluble conjugated forms or to metabolites that exhibit yet additional properties distinct from their parent compounds. However, deconjugation frequently occurs, regenerating endocrine-disrupting free estrogen compounds. It is challenging to fully understand the behavior and predict the fate of estrogenic compounds from dairy manure in soils because of variable and complex interactions with soil factors, as well as possible interactions among the different chemicals of emerging concern. This review focuses on the behavior of naturally occurring estrogen hormones present in dairy manure in the soil environment. Heightened understanding of the fate of these compounds in soil will enhance our ability to reduce their potential risks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".