Alfalfa (<i>Medicago sativa</i> L.) quality is improved from tractor traffic implemented during harvest
Bibliographic record
Abstract
Studies documenting the consequences of harvest traffic in alfalfa production have addressed soil and plant growth parameters. One response was larger leaf/stem ratios in plants that were trafficked, which suggests higher quality. To fully understand how harvest traffic affects alfalfa quality a need for further analysis is warranted. Our objectives were to quantify differences in plant quality between trafficked and non-trafficked plants through 4 years of alfalfa production and to determine when these differences occur. The experimental units were furrow-irrigated raised beds with four harvests per year in Youngston clay loam soil in Fruita, Colorado. A John Deere 2280 swather and a John Deere 2955 tractor, driven over the alfalfa 7 days after swathing, were used to create four traffic treatments; plants that were never trafficked, plants trafficked only by the swather, plants trafficked only by the tractor, and plants trafficked by both the swather and the tractor. Quality was determined by measuring relative feed value, acid detergent fiber, neutral detergent fiber, and crude protein using near infrared reflectance spectroscopy. Alfalfa trafficked by the tractor had increased quality throughout the 4 years of production.
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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 | 0.001 | 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".