Suspended Sediment Concentrations Downstream of a Harvested Peat Bog: Analysis and Preliminary Modelling of Exceedances Using Logistic Regression
Bibliographic record
Abstract
Acting as natural filters, peatlands are important wetland ecosystems in many northern countries, including Canada. To harvest peat, the vegetation must be removed and the harvested area ditched to drain and dry the peat. Drainage ditches are often designed to route water to settling ponds prior to releasing runoff into nearby water bodies. The present study investigated one key water quality variable, suspended sediment concentration (SSC), downstream of settling ponds in an actively harvested peatland. Time series of SSC for two spring seasons (2001-2002) were recorded at two sites using optical back scatterometers (OBS) calibrated in situ. SSC values exceeded the New Brunswick provincial guideline of 25 mg/L between 53.6 and 86.0% of the time. Even when the threshold was raised to relatively high values such as 500 mg/L, the percentage of exceedance remained relatively high (between 11 and 60%). A statistical model of SSC exceedance, based on logistic regression, was tested to investigate which hydrological forcings may explain high SSC values. Various independent variables were used in conjunction with an autoregressive component and were compared using different goodness of fit criteria. For a threshold of 500 mg/L, the best fit among all the logistic regression models tested included lag 1 and 2 autoregressive terms, as well as five-day cumulative precipitation, air temperature and three-day lagged discharge. The model was able to correctly predict 82% of exceedances.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".