Reducing the environmental impact : characterizing the efficiency of sedimentation basins downstream of harvested peat bogs.
Bibliographic record
Abstract
Harvested peat is a very lucrative industry in both Quebec and New-Brunswick (Canada). \nPeat enters in many potting mix used for horticulture. However, harvesting this resource \ncan have some impacts on the environment. \nTo harvest peat, industries need to drain the peat bog to dry the superficial layer. Then, it is \nharvested with industrial vacuums (Figure 1) and the underlying layer is allowed to dry. \nThe drained water can often be laden with suspended sediments (mostly organic peat fibers) \nthat may affect biota of the stream where it is discharged. To counter the problem, \nthis water does not go directly on \nthe stream but first flows through a \nsedimentation basin, built to reduce \nsuspended sediment loads. The \nprovince of New-Brunswick’s \nguidelines stipulates that concentrations \nshould not exceed 0.025g/L \ndownstream of those basins. Previous \nresearch showed that many \nCanadian streams and rivers have \nnatural background SSC that often \nexceed this value. This work focuses
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".