Characterization of runoff and infiltration from no-till soybeans with selected winter cover crops
Bibliographic record
Abstract
The influence of "living mulch" winter cover crops on soil loss, runoff amount and quality and soybean growth was studied at the Midwest Claypan Experimental runoff plots located on Mexico silt loam (Udollic Ochraqualf). Experimental treatments consisted of no-till soybeans with: 1) canada bluegrass (Poa compressa L.), 2) chickweed (Stellaria media h), 3) downy brome (Bromus tectorum L.), and 4) no cover crop (CK). Runoff, sediment, dissolved nutrients, soil water content, and plant growth characteristics were measured. For chickweed (CW), canada bluegrass (CB) and downy brome (DB) treatments, runoff was reduced 66, 56, and 80 percent (P [less than] 0.01), and soil loss was decreased 61, 97, and 95 percent (P [less than] 0.01), respectively, vs. the CK treatment. Concentrations of dissolved NH4+-N and P04-3-P in runoff water from cover crop plots were 2 to 2.8 times higher than the CK (P [less than] 0.05). Runoff from the CK had a higher concentration of dissolved No3--N. Total amounts of dissolved N03--N losses were significantly decreased by 71, 73, and 76 percent (P [less than] 0.01) and NH4+-N losses reduced by 40, 36, and 46 percent (P [less than] 0.10) for treatments of CW, CB, and DB vs. the CK, respectively. P04-3-P losses also were decreased by 50, 21, and 39 percent for CW, CB, and DB vs. CK, but differences were not significant (P [greater than] 0.10). Lower plant populations and delayed plant development decreased soybean yield in cover crop treatments from 18 to 62 percent (P [less than] 0.01) vs. the CK.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".