Removal of nutrients from aquaculture wastewater using cattail (<i>Typha</i> spp.) constructed wetlands
Bibliographic record
Abstract
Abstract The aquaculture industry is among the fastest growing food production sectors in the world. Land‐based aquaculture systems continue to increase in popularity as they offer the benefits of controlling diseases, managing water quality, and minimizing threats to wild populations of fish. However, these systems discharge wastewater high in N and P. The ability of cattail (Typha spp.) constructed wetlands (CWs) to remove N and P from aquaculture wastewater (AWW) was examined here. Cattail CWs were established in mesocosms and had a gradient of AWW applied weekly for a total of 5 weeks. Total N and P loadings ranged from 13.7 to 209.2 mg m−2 and 3.01 to 45.97 mg m−2 over 28 days, respectively. Additions of AWW did not cause elevations in total dissolved N, total ammonia N, or nitrite N in CW water; however, concentrations of nitrate N and P in CW water were related to nutrient loading conditions. Elevations in P persisted for 3–4 weeks among high nutrient loading treatments, providing an opportunity for eutrophic conditions to develop in CW systems. However, after 33 days of treatment, >95% total P concentration reduction was achieved in all mesocosms with final concentrations <0.05 mg L−1, equivalent to reference conditions. High‐loading treatments achieved greater P load reduction (856.8–955.0 mg m−2 year−1) than low‐loading and reference treatments (591.7–792.7 mg m−2 year−1). This study demonstrates the effectiveness of cattail CWs to remove nutrients during AWW treatment and highlights the potential for end‐of‐season use in northern climates, providing insights regarding the operational timeline of such systems.
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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".