Assessing soil erosion and sediment flux using 137Cs within topographically complex landscapes of Prairie Pothole Region of Canada
Bibliographic record
Abstract
Soil erosion and associated land degradation, accelerated by anthropogenic activities, are serious worldwide environmental problems that can reduce crop productivity and cause on- and off-site damages resulting from sediment transport and deposition. A common feature of the agriculture-dominated landscape of the Prairie Pothole Region in western Canada is depressional wetlands (typically 10 to 20, small seasonal to permanent waterbodies per square kilometer). These wetlandscapes are fragile agro-ecosystems that have been disturbed and impacted through human-induced soil erosion, receiving influxes of sediment and associated constituents (e.g., phosphorus, organic carbon and pesticides) from the surrounding local and regional sources (e.g., cultivated fields). The general objective of this study was to estimate the severity of soil loss and sedimentation rates, and sediment flux from surrounding cultivated fields into wetlands. The study area included two sub-watersheds in the Prairie Provinces of Manitoba and Alberta, within the Canadian portion of the Prairie Pothole Region. Soil and sediment cores were collected along multiple transects from the uppermost portion of each catchment to the central area of the wetland. Transects were distributed within catchments to capture the variability resulting from the topographic complexity. The results of this study demonstrated high rates of soil loss in the upper slope and middle slope positions, and high rates of deposition in lower slope and foot slope positions of wetlandscapes. Furthermore, the areal average of transect data provided sediment delivery ratio estimates of 57% for Manitoba and 35% for Alberta, which indicates that a relatively high amount of the mobilized sediment was exported beyond the cultivated fields towards the wetlands, mostly deposited in riparian areas.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".