Evaluating the potential of effluent extracts from pulp and paper mills in Canada, Brazil, and New Zealand to affect fish reproduction: Estrogenic effects in fish
Bibliographic record
Abstract
The authors examined the potential of pulp mill effluent from pulp-producing countries (Canada, Brazil, New Zealand) to affect fish reproduction. Specifically, the estrogenic effects in juvenile rainbow trout (Oncorhynchus mykiss) pulse-exposed to 11 different mill effluent extracts (intraperitoneal injections of solid-phase extraction-dichloromethane nonpolar fraction). The results indicated that effluent extracts were estrogenic in juvenile trout irrespective of the gender, as reflected by increasing level of plasma vitellogenin (VTG; Brazil > New Zealand > Canada). Despite the high variability observed among mills, differences in VTG levels were related to the type of mill process (kraft > elementary chlorine-free kraft > thermomechanical pulping). Moreover, effluent treatments did not appear to significantly decrease VTG induction. A consistent estrogenic effect was observed in those mills that process a combination of feedstocks (softwood and hardwood), with the highest increase in VTG related to eucalyptus feedstock. The results demonstrate significant estrogenic effects of pulp mill effluents on chronically exposed juvenile trout, suggesting that in vivo metabolic activation of precursors is necessary to cause the observed increases in VTG levels. This molecular estrogenic response provides a useful starting point for predicting population-level impacts through the adverse outcome pathway methodology. Environ Toxicol Chem 2017;36:1547-1555. © 2016 SETAC.
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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 | 0.001 | 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".