Characterizing and modelling the trapping efficiency of sedimentation basins downstream of harvested peat bog
Bibliographic record
Abstract
Peat moss harvesting is an important industry in Canada. To harvest peat, the water table of the peatland must be lowered to allow the surficial peat layer to dry and be harvested. Drainage water can contain suspended sediments, so at most harvesting sites, the water is routed through sedimentation basins. This work focuses on characterizing and modelling their trapping efficiency. Eight basins with different volume/watershed area ratios (705 to 4170 m3/km2) were studied in three Quebec regions. Suspended sediment concentration and discharge were monitored up- and downstream of each basin during the ice-free season. Basins with high ratios of volume/drainage area, multiple basin configurations (i.e., two basins in series or in parallel), and those equipped with a flow regulation structure were more efficient than regular basins. Moreover, the nature of sediments (size, decomposition level, and organic content) influences loads, but not the trapping efficiency. Finally, two empirical models developed to predict trapping efficiency of municipal sedimentation basins were tested and adapted for basins that capture peat sediments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".