Prediction of the Particle Size Distribution of Eroded Sediment from Construction Sites Using Artificial Neural Network Software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main objective of this study was to develop an artificial neural network (ANN) model to more accurately predict the event-specific particle size distribution (PSD) of eroded sediments in storm water runoff from construction sites. The eroded sediment PSD is a key design parameter for erosion and sediment control best management practices (BMPs). To complete this task, two active construction sites in Ontario were monitored over a period of two years. This data was supplemented with data collected from laboratory scale experiments on 14 different soils and data from watershed scale stream sediment PSD data. Parent and eroded PSDs were quantified by fitting each to a log normal distribution. The developed ANN model was able to much more accurately (compared to existing regression models) predict the PSD of eroded sediment using easily obtainable inputs (parent log normal parameters, USLE K, C, and P factors, rainfall EI30, flow path, and slope). The ANN has the potential to be used by erosion and control specialists to determine the range of particles to target throughout BMP design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it