Dynamics of suspended sediment flux on the rivers bandon and owenabue, ireland
Bibliographic record
Abstract
Monitoring, analysis and modelling of suspended sediment transport in riverine systems has received much attention in recent decades.Quantitive analysis techniques have been applied to allow river basin managers to assess suspended sediment transport and its importance in transporting a range of pollutants including excess metals and nutrients, which negatively impact on aquatic systems.In this paper, an analysis of the suspended sediment fl ux (SSF) dynamics is presented for the small and medium sized catchments of the River Owenabue and the River Bandon, respectively.The results contribute to the fi lling of a research gap in an Irish context where SSF analyses at this scale have not previously been presented.River fl ow rate and turbidity data were collected on both rivers for one full year.Turbidity was found to be an excellent surrogate for suspended sediment concentration on both rivers with r 2 values greater than 0.87 found.The annual SSFs passing the gauging stations for the River Bandon and River Owenabue were found to be 6012 and 2635 tonnes, respectively.SSF analysis reveals that 85% and 92%, respectively, of the total annual fl ux was transported on the Rivers Bandon and Owenabue during the high fl ow storm-based events.SSFs were investigated at the seasonal, intra-and inter-event scales -the largest 10 SSF events were identifi ed and analysed.Seasonally, most of the fl ux is delivered in the winter months from October to March.At the intra-event scale, it was found that events of similar fl ow magnitude do not necessarily transport similar fl ux levels.Total fl uxes were found to decrease for sequential events as a result of sediment exhaustion.At the inter-event scale, hysteresis is observed and analysed for the identifi ed events with clockwise hysteretic loops dominating implying that suspended sediment transport is generally supply limited, and that the primary sources of sediment are located near or within the river.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".