Detoxification of Disperse Yellow 3 Contaminated Wastewater Using Constructed Wetlands
Bibliographic record
Abstract
The textile manufacturing industry allows for the release of toxic dyes into the environment. Environment and Climate Change Canada has focused on the dye Disperse Yellow 3, which is imported in large quantities as a potential concern for both human health, as well as the environment. Constructed wetlands are a low maintenance and cost-effective method of treating contaminated wastewater. To test their effectiveness at degrading the dye, a 72-hour algal bioassay utilizing the green algae species Raphidocelis subcapitata was used to determine the EC₅₀ growth for Disperse Yellow 3. The bioassay was then used to determine if constructed wetlands planted with or without the native species blue flag iris (Iris versicolor) could reduce the toxicity of Disperse Yellow 3 at various concentrations based around the EC₅₀ growth. The 72-hour algal bioassay determined that the EC₅₀ growth for Disperse Yellow 3 was 0.13 mg/L. The results found that constructed wetlands were capable of reducing the toxicity at all concentrations tested such that there was no statistically significant difference in the toxicity. While plants' presence in the wetlands did not provide a significant effect, constructed wetlands may be a feasible option to be used as a pre-treatment process for textile mills prior to release.
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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 | 0.000 | 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".