Landscape changes and watershed erosion in Prince George's County, Maryland
Bibliographic record
Abstract
Abstract This study evaluated erosion rates and sediment production in streams, and factors potentially influencing them throughout the Anacostia, Patuxent, and Potomac (non‐Anacostia) River watersheds within Prince George's County, Maryland, US. As part of the County's watershed‐scale biological monitoring program, from approx. 1999 to 2008, permanent monuments were established to allow measurement of stream channel cross‐sectional (XS) area. The intent of this study was to characterize the intensity and spatial distribution of fluvial geomorphic instability across the county and use the results to target and plan stormwater management and stream restoration actions. For this study, 78 stream locations were re‐surveyed in 2020, representing a time lapse of from 12 to 21 years. Data collected included XS dimensions, modified Wolman 100‐particle pebble counts, and reach‐specific soil bulk density. Land use/land cover data were compiled from the National Land Cover Dataset (NLCD), precipitation from the National Weather Service Center for Environmental Information (NCEI), and soils from the Natural Resources Conservation Service Web Soil Survey (NRCS/WSS). We calculated percent change in XS area, rates of erosion, sediment yield, and assigned geomorphic classifications, and interpreted them in the context of spatial positions relative to changes in land cover characteristics. Sediment yields among the 78 reaches exhibited a combination of those undergoing enlargement/erosion (67.9%), reduction/deposition (25.6%), and the remaining 6.4% with essentially no change over the period of record. Of the top 20 most geomorphically active reaches surveyed in the County, 12 are in the Anacostia River basin, with the other scattered among the Patuxent River and Potomac River basins.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".