Evaluating the Potential of Effluents and Wood Feedstocks from Pulp and Paper Mills in Brazil, Canada, and New Zealand to Affect Fish Reproduction: Chemical Profiling and In Vitro Assessments
Bibliographic record
Abstract
This study investigates factors affecting reproduction in fish exposed to pulp and paper mill effluents by comparing effluents from countries with varying levels of documented effects. To explore the hypothesis of wood as a common source of endocrine disrupting compounds, feedstocks from each country were analyzed. Analyses included in vitro assays for androgenic activity (binding to goldfish testis androgen receptors), estrogenic activity (yeast estrogen screen), and neurotransmitter enzyme inhibition (monoamine oxidase and glutamic acid decarboxylase). Chemical analyses included conventional extractives, known androgens, and gas chromatograph index (GCI) profiles. All effluents and wood contained androgenic activity, particularly in nonpolar fractions, although known androgens were undetected. Effluents with low suspended solids, having undergone conventional biotreatment had lower androgenic activities. Estrogenic activity was only associated with Brazilian effluents and undetected in wood. All effluents and wood inhibited neurotransmitter enzymes, predominantly in polar fractions. Kraft elemental chlorine free mills were associated with the greatest neurotransmitter inhibition. Effluent and wood GCI profiles were correlated with androgenic activity and neurotransmitter enzyme inhibition. Differences in feedstock bioactivities were not reflected in effluents, implying mill factors mitigate bioactive wood components. No differences in bioactivities could be discerned on the basis of country of origin, thus we predict effluents in regions lacking monitoring would affect fish reproduction and therefore recommend implementing such programs.
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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: yes | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Bench or experimental | high |
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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, 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".