Trace organic compounds in rivers, streams, and wastewater in southeastern Alberta, Canada
Bibliographic record
Abstract
We selected 14 anthropogenic organic compounds representing major classes of potential contaminants for analysis to determine their occurrence in the South Saskatchewan River and its tributaries near irrigated farmland and the only urban center in southeast Alberta, Canada. Agriculture and urban runoff and discharges seem to have little impact on the quality of surface water based on samples taken above and below Medicine Hat/Redcliff in the South Saskatchewan River and local tributaries. Samples of river water, tributary water, and raw and treated wastewater taken over a period of 3 years allowed an estimation of the impact of trace organic compounds from urban and agricultural activities on water quality. Of the 14 compounds investigated, 10 were detected in concentrations above the detection limit in at least one surface water sample and 9 at concentrations above the detection limit in sewage samples. The wastewater treatment plant removed indicator compounds to varying degrees, and the volume of treated effluent discharge was <1% of the river, even during the lowest flow conditions, thereby minimizing potential impact. Discharge in the river and tributaries varied by an order of magnitude over the period of study, including 2 major flood events in the South Saskatchewan River. Potential health or environmental effects were difficult to evaluate from a regulatory perspective because few guidelines are available for reference.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".