Rapid mineralization of the endocrine-disrupting chemical 4-nonylphenol in soil
Bibliographic record
Abstract
Abstract The persistence of the xenoestrogenic compound 4-nonylphenol in agricultural, noncultivated temperate, and Arctic soils was assessed in laboratory microcosm incubations. At 30°C, [ring-U-14C]4-nonylphenol was rapidly mineralized without a lag in six soils tested. A sandy loam agricultural soil was chosen for more detailed study. The 4-nonylphenol mineralization did not occur in autoclaved soil. The response of 4-nonylphenol mineralization to variation in temperature and moisture content was consistent with an aerobic biological mechanism of degradation. Mineralization of [ring-U-14C]4-nonylphenol was rapid in the concentration range of 1 to 250 mg/kg soil. Sludge solids did not inhibit 4-nonylphenol mineralization, although sewage sludge at high concentrations was inhibitory, apparently because of high biological oxygen demand. Gas chromatographic-mass spectrometric analyses of extracts prepared from soil incubated with commercial nonylphenol indicated that all detectable isomers were degraded. In summary, these results indicate that microorganisms that can metabolize 4-nonylphenol are found in a wide variety of soils, including two originating from the Canadian Far North, which presumably have not been exposed anthropogenically to this chemical. We conclude that 4-nonylphenol should be generally biodegradable in well-aerated arable soils.
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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".