Assessing pharmaceuticals and personal care products (PPCPs) and their environmental risk in a lake in North Quebec
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the occurrence and ecological risks of pharmaceuticals and personal care products (PPCPs) in lake Osisko, northern Quebec, Canada. Ten sites, including storm outfalls and lake stations, were sampled for three summer campaigns. Results showed clear spatial and temporal variation, with storm outfalls acting as major entry points of contaminants into the lake. Among the 11 targeted PPCPs, caffeine, salicylic acid, and methylparaben were most frequently detected in surface water and sediments. Their presence highlights both urban runoff and recreational activities as important pollution sources. Environmental risk assessment revealed that caffeine posed a high risk to aquatic organisms, while ciprofloxacin presented medium risk, and other compounds were classified as low risk when assessed individually. Although some PPCPs are short-lived in the environment, their continuous inputs sustain measurable concentrations that may impact sensitive species. This study provides evidence-based insights for implementing monitoring and pollution control strategies to protect the ecological and recreational functions of Lake Osisko. ∗ PPCPs- Pharmaceuticals and Personal Care Products; SPE- Solid Phase Extraction; LC-MS/MS- Liquid Chromatography- Tandem Mass Spectrometry. • First targeted monitoring of PPCPs in Lake Osisko, Quebec. • Storm outfalls were the main entry points, with higher PPCP levels than lake stations. • Caffeine posed high ecological risk, ciprofloxacin medium, with others showed low risk. • Continuous PPCP inputs highlight the need for monitoring and pollution control strategies.
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.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it