Sediment dynamics in the fluvial lakes of the St. Lawrence River: accumulation rates and characterization of the mixed sediment layer
Bibliographic record
Abstract
Permanent sedimentation (210Pb and 137Cs), sediment mixed layer thicknesses, and mixing coefficients (7Be) were measured in the St. Lawrence River in order to evaluate the importance of sediment retention in the particulate matter budget and to characterize the system's resilience to changing contaminant loads. Net sediment accumulation (1 to > 18 kg·m-2·year-1) is observed at most sites deeper than 4.5 m located outside the main channels. Annual sediment retention in the lakes ranges from 1.5% (Lake St. Pierre) to 17% (Lake St. Francis) of their total load of suspended solids. 7Be profiles indicate that the average mixed layer thickness, mixed layer mass, and mixing coefficient are 3.3 ± 0.2 cm, 17.8 ± 1.7 kg·m-2, and 14.9 ± 2.8 cm2·year-1, respectively. The average depth of the long-term (approximately 5 years) mixed layer determined from the 137Cs : anthropogenic Pb ratio is 5.1 ± 0.4 cm, corresponding to 30.6 ± 4.6 kg·m-2. Because the mixing coefficient in superficial sediments is relatively high, and because annual particulate matter loading to the river is comparable with its mixed sediment inventory, the system is expected to have a rather short memory of past conditions and to recover rapidly (2-5 years) following a decrease in contaminant loading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".