Analysis of aryl hydrocarbon receptor ligands in kraft mill effluents by a combination of yeast bioassays and CG-MS chemical determinations
Bibliographic record
Abstract
Aryl Hydrocarbon Receptor (AhR) ligands also known as dioxin-like compounds, constitute a substantial part of the total toxicity from many pollution sources, including pulp mill effluents. The aim of this article was to evaluate dioxin-like activity in different kraft mill effluents by a combination of yeast bioassays and gas chromatography-mass spectrometry (GC-MS) chemical analysis. The study includes kraft mill effluents from three sources of raw material: Pinus radiata, Eucalyptus globulus and a combination of both (50% each). The Recombinant Yeast Assay (RYA) showed an effective concentration of AhR ligands more than 30-fold higher in Eucalyptus globulus than in Pinus radiata effluents. Our results suggest that specific ligands, rather than the total amount of extractive material, determined the observed activity. Analysis of extract composition by GC-MS indicated that moderately hydrophobic aromatic compounds were likely responsible for the observed dioxin-like activity. In particular, benzaldehyde derivatives appeared as candidates for eliciting the observed dioxin-like activity in pulp mill effluents, giving their structural properties and their high concentration in AhR ligand-rich samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".