Comparison of runoff quantity and quality under annual cropping and forages
Bibliographic record
Abstract
Conversion from annual cropping to perennial forages may be a beneficial management practice (BMP) to reduce runoff quantity and improve surface water quality. Runoff variables were determined in two 30 ha producer's fields over 4 years (2004–2007) using a rainfall simulator. Field 1 was cropped to barley (Hordeum vulgare L.) in 2004 and then an alfalfa (Medicago sp.) and grass mix used for hay and fall grazing was grown from 2005 to 2007. Field 2 was cropped to barley-winter triticale (WT) (Triticosecale Rimpavi Wittm.) in 2005 and alfalfa-grass for hay was grown in 2006 and 2007. Runoff variables measured were runoff depth, electrical conductivity, and concentrations and loads of total suspended solids, total N, total P, and dissolved reactive P fractions. Conversion from barley to alfalfa-grass for hay and fall grazing in Field 1 resulted in reductions for only 13% of the 10 runoff variables, 33% of runoff variables were unaffected, and 53% of variables were significantly increased. Conversion from barley-WT to alfalfa-grass used for hay in Field 2 significantly reduced 25% of the 10 variables, 40% were unaffected, and 35% were significantly increased. Converting from annual cropping to forage did not improve the majority of runoff variables.
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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 | 0.000 | 0.000 |
| 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".