Inundation Patterns of Farmed Pothole Depressions with Varying Subsurface Drainage
Bibliographic record
Abstract
Highlights Farmed pothole depressions in the Des Moines Lobe were observed to fill due to runoff and shallow subsurface flow. Six of the eight observed potholes flooded for five or more days some time during the two years of observation. Subsurface drainage and surface inlets reduced but did not prevent yield-limiting flooding in the observed potholes. Abstract. The prairie pothole region (PPR) ranges from central Iowa to the northwest into Montana and south central Canada, totaling around 700,000 km2. This area contains millions of potholes, or enclosed topographical depressions, which often inundate with rainfall. Many are located in areas that have been converted to arable agricultural land through installation of artificial drainage. However, even with drainage, potholes will pond or have saturated soil conditions during and after significant rain events. The portion of the PPR that extends into Iowa is known as the Des Moines Lobe. In this two-year study, surface water depth data were collected hourly from eight prairie potholes in the Des Moines Lobe in central Iowa to determine the surface water hydrology. These potholes included surface and subsurface drained row crops and undrained retired land, allowing for drainage comparisons. Inundation lasted five or more days at least once at six of the eight potholes, including four potholes with surface inlets and subsurface drainage, which resulted in four of fourteen growing seasons not producing a yield in part of the pothole. Water balances of four different drainage intensities showed increased infiltration due to subsurface drainage and up to 78% of outflow due to surface inlet drainage. Overall, drainage decreased the number of average inundation days, but heavy precipitation events still caused lengthy inundation periods that resulted in crop loss. Keywords: Farmed wetlands, Prairie pothole, Tile drainage, Water balance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".