Occurrence and Concentrations of Estrogenic Phenolic Compounds in Surface Waters of Rivers Flowing into Masan Bay, Korea
Bibliographic record
Abstract
The estrogenic phenolic compounds, nonylphenol (NP), octylphenol(OP), bisphenol A (BPA) and nonylphenol mono- and diethoxylate (<TEX>$NP_{1-2}EO$</TEX>) were analyzed in 24 surface water samples from six rivers flowing into Masan Bay. All of the phenolic compounds were detected in all six rivers in high concentrations. The most abundant compound was <TEX>$NP_{1-2}EO$</TEX> (86.0%), followed by NP (<TEX>$10.1 \%$</TEX>), BPA (<TEX>$3.6\%$</TEX>) and OP (<TEX>$0.3\%$</TEX>). The levels of phenolic compounds were 1.42-22.70 <TEX>${\mu}g$</TEX>/L for <TEX>$NP_{1-2}EO$</TEX>, 0.15-1.68 <TEX>${\mu}g$</TEX>/L for NP, 0.024-0.610 <TEX>${\mu}g$</TEX>/L for BPA and 0.003-0.067 <TEX>${\mu}g$</TEX>/L for OP. Especially, high concentrations were recorded in the rivers that pass through industrial complexes. The concentrations of phenolic compounds observed in these river waters were 1-2 orders of magnitude lower than the reported acute toxicity levels (hundreds of micrograms per liter). However, they were only slightly lower than the chronic toxicity levels. Most of the water samples also exceeded the Canadian nonylphenolic compounds water quality guideline, 1 <TEX>${\mu}g$</TEX>/L, for the protection of aquatic life and the maximum permissible concentrations (MPC), 0.33 <TEX>${\mu}g$</TEX>/L for NP and 0.12 <TEX>${\mu}g$</TEX>/L for <TEX>$NP_{1-2}EO$</TEX>.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".