Comparison of Acoustic to Optical Backscatter Continuous Measurements of Suspended Sediment Concentrations and Their Characterization in an Agriculturally Impacted River
Bibliographic record
Abstract
The increased soil loss in an agricultural watershed raises challengers for river water quality and a reliable automated monitoring for suspended sediment concentrations (SSC) is crucial to evaluate sediment budgets variation in systems. The aims of this study were (1) to test if an acoustic doppler current profiler (ADCP) would give similar results to turbidity probe measurements as a high frequency monitoring tool for suspended sediment; and (2) to analyze the relationship between sediment drivers and SSC in a typical agricultural drainage basin. The acoustic and optical backscatter sensors were used to collect SSC data during the ice-free seasons of four consecutive years in the Dunk River (PEI, Canada). The slopes of the relationships between the two SSC indirect measurements were not significantly different than 1. Correlations between SSC and hydro-meteorological variables showed that the high SSC values were more associated with the streamflow and water velocity than precipitation. This study highlighted the great potential of ADCP for the continuous monitoring of suspended sediment in an agricultural watershed. For summer periods the prevalence of clockwise hysteresis (74.1% of measured rainstorm events with SSC > 25 mg L−1) appeared related to rainstorm behaviors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".