Détection et caractérisation des macro-, micro- et nanoplastiques dans l'estuaire et golfe du fleuve Saint-Laurent, Canada
Bibliographic record
Abstract
The St Lawrence River Canada), (despite its potential role in the accumulation and transfer of plastic debris from land to ocean remains poorly documented in this regard. This study aims to fill this gap by providing a baseline assessment of macro micro and nanoplastics MNP) (concentrations in the Estuary and Gulf of St Lawrence EGSL). While macroplastics are generally characterized by FTIR spectroscopy, mass quantification is essential to accurately assess MNP fluxes and their toxicological impacts. In this context, pyrolysis coupled with gas chromatography tandem mass spectrometry Py GC MS/ MS) has proven to be an optimal method. This work helps to overcome the specific analytical challenges associated with the study of MNP in complex environmental matrices, thus providing a framework for improving the reliability and sensitivity of environmental analyses.In environmental terms, this study provides for the first time a detailed overview of the mass concentration, composition and distribution of MNP in surface water and blue/common mussels (Mytilus spp.) from the EGSL. The results show macroplastics concentrations of 0,17 ± 0,11 items/m², MNP in water of 3,800 ± 2,700 ng/L and in blue mussels of 93 ± 53 ng/mg dry weight.From the societal point of view, this study provides unprecedented data on a key Canadian ecosystem, helping to raise public awareness and guide environmental management policies. By reporting on macro and MNP contamination, it provides valuable data for local and global pollution reduction strategies, and for sustainable management of the St Lawrence's natural resources.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".