Characterizing phosphorus removal in passive waste stabilization ponds in Arctic communities
Bibliographic record
Abstract
A majority of communities in the Canadian territory of Nunavut rely on passive waste stabilization ponds (WSPs) for domestic wastewater treatment. Little research has been conducted on the treatment performance of these systems. Therefore, in response to impending federal wastewater regulations, a research program was conducted in order to characterize contaminant removal, with phosphorus a contaminant of particular concern. The performance of WSPs in the Arctic communities of Kugaaruk, Pond Inlet, Grise Fiord, and Clyde River was evaluated from 2011 to 2014. Removal of total phosphorus was highly variable, ranging from 24% (Pond Inlet, 2014) to 76% (Grise Fiord, 2011). The average removal efficiency was 44%. Effluent total phosphorus concentrations generally exceeded 7 mg P/L, partly due to elevated raw wastewater concentrations. Over the course of the treatment season (defined as June to September, when the WSP is thawed), limited additional total phosphorus removal was observed. A fractionation analysis of WSP sediments showed that organic phosphorus and phosphorus bound to aluminum and iron were the predominant forms, which provided insight into primary treatment mechanisms. Further studies on these mechanisms are needed in order to optimize Arctic WSP treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".