Influence of Lead Pollution on Cohesive Soil Erodibility using Jet Erosion Tests
Bibliographic record
Abstract
Recent researches were investigated the high concentrations of Lead in Baghdad soils due to the emissions from Leaded fuel of cars, generators, and the industrials. These high concentrations in addition to their impact on human health may impact on the landscape and streambanks and may cause significant issues on soil erodibility. The erosion rate of cohesive soil was usually estimated using two alternative models, excess shear stress model which is depended on two major soil parameters: the critical shear stress, τc, and the erodibility coefficient, kd, and Wilson model which is depended on two mechanistic soil parameters: b0 and b1. A new miniature version of Jet Erosion Test (“mini” JET) was performed to derive both model parameters. The objective of this study was to investigate the influence of Lead pollution on cohesive soil erodibility using “mini” JET under controlled laboratory setups to predict soil erodibility. In order to observe the Lead contamination on soil erodibility, soil samples were mixed with different quantities of Lead concertation and the samples were packed at ASTM standard mold on two different bulk densities. Results show that the Lead pollution increased soil erodibility when the concentration of Lead increased. An inverse relationship between excess shear stress parameters kd and τc was observed as well as between Wilson model parameters b0 and b1. The Wilson model parameters were closely resembled the empirical excess shear stress parameters with benefit that Wilson model parameters are mechanistic parameters.
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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 | 0.001 |
| 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) | 0.001 | 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".