Assessing soil phosphorus concentrations and using blind inlets amended with iron steel shavings to reduce phosphorus export in farmed potholes
Bibliographic record
Abstract
Prairie potholes are common land features in north-central Iowa that formed over 10,000 years ago during the most recent glacial retreat. The Prairie Pothole Region extends beyond north-central Iowa into Minnesota and Canada and as far west as Montana. This region has many closed depressions known as potholes that can become inundated with water during rain events. Today, many of these potholes contain artificial subsurface drainage to lower the water table, allowing the potholes to be farmed. Surface drainage intakes are also often used within farmed potholes to convey surface water directly to the subsurface tiles without infiltrating through the soil. Though drained, farmed potholes often become inundated after large rain events. Research has shown that pothole surface waters are enriched with dissolved reactive phosphorus (DRP) and total phosphorus (TP). Therefore, surface intakes directly export the phosphorus-enriched surface water. To reduce phosphorus (P) export from surface intakes, a modified blind inlet containing iron steel shavings replaced a surface intake within a farmed pothole. Steel shavings were used as the phosphorus sorption material (PSM) and accounted for 8% of the total media weight. The remaining 92% of the media was composed of pea gravel to allow proper infiltration into the structure. A bench-scale P adsorption study was conducted on the blind inlet media, and the media removed at least 98% of the initial DRP when the initial concentrations ranged from 0.2 to 25 mg L-1. The constructed modified blind inlet removed an average of 82.6% of the influent DRP concentration and 61.9% of the influent TP concentration. Overall, the combined results from this study demonstrate that blind inlets containing steel shavings can replace surface intakes and reduce P export from farmed potholes. This research guides future designs and implementations of these P-removal structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".