Disinfection Performance in Wastewater Stabilization Ponds in Cold Climate Conditions: A Case Study in Nunavut, Canada
Bibliographic record
Abstract
Disinfection processes in passive wastewater treatment systems, which are dependent on natural purification, can be greatly influenced by environmental factors. In the Canadian Arctic, the passive systems face more challenges due to the extreme environmental conditions. The new Wastewater Systems Effluent Regulations (WSER) were implemented in Canada in 2012. Currently, they do not apply in the far North due to the limited wastewater treatment infrastructure in northern communities. In the summer of 2015, a field investigation was conducted to Pond Inlet, Nunavut, to assess the pathogen removal and inactivation of a wastewater stabilization pond (WSP). Sunlight disinfection was considered only effective at the water surface. The system achieved 0.76–1.2 log removal of E. coli and 0.79–1.02 log removal of total coliforms during the treatment season in 2015. Prior to annual decant, the average concentration of E. coli was 1.3 × 106 CFU/100 mL in the WSP, which exceeded discharge guidelines of 104 to 106 CFU/100 mL set by the Nunavut Water Board (NWB). Existing WSP disinfection models, which were typically designed for temperate or tropical regions, were selected to study their viability to predict the pathogen removal of Arctic WSPs. In general, the models over-predicted disinfection performance by an order of magnitude or more, and some were unable to replicate trends in the data. A modified model for northern WSPs should be developed in order to accurately predict disinfection performance.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".