Protecting our Great Lakes: assessing the effectiveness of wastewater treatments for the removal of chemicals of emerging concern
Bibliographic record
Abstract
The Great Lakes and their connecting channels form the largest fresh surface water system on earth. Over the past 10 years, focus on environmental monitoring has shifted to an array of recently discovered compounds known as ‘chemicals of emerging concern’ (CEC). These chemicals are found in products used daily in households, businesses, agriculture and industry, such as flame retardants, pharmaceuticals, personal care products, and pesticides. Wastewater treatment plants are among the important pathways by which CEC enter the Great Lakes, with concentrations highest in the vicinity of wastewater discharges. Treated sewage is often discharged into the nearshore waters, which also provide a source of drinking water to the public. In 2009–2011, the International Joint Commission addressed the need to assess the effectiveness of existing wastewater treatment technologies in the basin to remove CEC, as well as to gain insight on potential advanced technologies to improve their removal. This assessment encompassed three major activities, development of an inventory of municipal wastewater treatment plants that discharge in the basin; a survey of detailed operational data for selected wastewater facilities; and a comprehensive literature review and analysis of the effectiveness of various wastewater treatment technologies to remove chemicals of emerging concern.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.002 |
| 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 | 0.001 | 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 teacher head, 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".