Land Use and Riparian Effects on Prairie Wetland Sediment Properties and Herbicide Sorption Coefficients
Bibliographic record
Abstract
Sorption of commonly used herbicides by wetland sediment can provide important information for herbicide fate modeling. The influence of sediment properties on herbicide sorption as a result of different land uses in the wetland catchment is unclear. We examined the effects of land use on the physiochemical properties of wetland sediments and the associations between these sediment properties and herbicide sorption characteristics. Bottom sediments were sampled in 0- to 5- and 5- to 10-cm sections from 17 wetlands under five different land use classes: (i) ephemeral wetlands with no riparian vegetation in a cultivated catchment (ECNR), (ii) ephemeral wetlands with riparian vegetation in a cultivated catchment (ECR), (iii) ephemeral wetlands in a grassland catchment established 4 yr ago (E4G), (iv) ephemeral wetlands in a brome grass catchment established 20 yr ago (E20G), and (v) semi-permanent (SP) wetlands in a multiple-land-use catchment. Sediments were analyzed for total organic carbon (TOC), total inorganic carbon (TIC), pH, electrical conductivity, exchangeable cations (EXCAT), total cation exchangeable capacity (CEC), and percent clay (%clay). Sediment herbicide sorption partition coefficient (Kd) was measured for trifluralin, atrazine, 2,4-D, and glyphosate. The sorption of the herbicides in the sediment increased in the order of 2,4-D < atrazine < glyphosate < trifluralin. The sorption of 2,4-D, atrazine, and trifluralin was positively correlated to TOC, EXCAT, and CEC but negatively correlated to %clay. Glyphosate sorption was negatively correlated to pH, TIC, EXCAT, and %clay. Overall, wetland sediments that were recently cultivated (ECNR and E4G) had lower TOC, TIC, EC, EXCAT, CEC, and Kd values (2,4-D, trifluralin, and atrazine) than sediments that had not been recently cultivated (ECR, E20G, and SP). The ECR wetland sediments had the largest Kd for all four herbicides, suggesting that land use and riparian vegetation have a significant impact on herbicide sorption.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".