Quantification of Plant Sterols in Pulp and Paper Mill Effluents
Bibliographic record
Abstract
Abstract Pulp and paper mill effluents (PPMEs) may contain high levels of otherwise naturally occurring organic pollutants such as plant sterols, which are suspected endocrine disrupting chemicals. Exposure to such chemicals may cause various physiological and morphological abnormalities that have been reported in the fish and other aquatic life inhabiting PPME receiving waters. Plant sterols, or phytosterols, form a constituent of wood extractives that may be released into the effluents during the pulping and paper making processes. Isolation and analysis of sterols from the complex mixture of PPMEs is challenging and standard analytical protocols do not exist. The need for having a reliable method for analyzing a particular environmental contaminant such as plant sterols cannot be overemphasized. In the present study a technique was modified for reliable analysis of PPME sterols. The technique involves liquid-liquid extractions using methyl-t-butyl ether and trimethyl-silylation derivatizations of the extracted sterols. Identification and quantification of the PPME sterols were accomplished by gas chromatography and mass spectrometry. Analytical problems were resolved by conducting multiple extractions, drying the sterol extracts, and redissolving and silylating the extracts at an increased derivatization temperature of 70°C. This shortened the suggested incubation period from 12 to 4 h. The modified technique offered improved method sensitivity and reproducibility, and successfully quantified campesterol, β-sitosterol, β-sitostanol, stigmasterol, stigmastanol, cholesterol, and ergosterol in PPMEs. Primary and secondary treated PPMEs analyses suggested 800 ± 190 µg/L total sterols in primary effluents, and 211 ± 90 µg/L in biologically treated final effluents. β-Sitosterol, β-sitostanol, and campesterol alone accounted for about 80% of the total sterols. A general comparison of the sterols in primary and secondary effluents suggested about 73% removal across the secondary treatment systems sampled.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.005 | 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".