Lower levels of harvest traffic on alfalfa (<i>Medicago sativa</i>L.) have minimal impact on long-term yields
Bibliographic record
Abstract
Rechel, E., Novotny, T. and Ott, R. 2012. Lower levels of harvest traffic on alfalfa (Medicago sativa L.) have minimal impact on long-term yields. Can. J. Plant Sci. 92: 1253–1258. Studies quantifying the effect of harvest traffic on alfalfa yield often only analyze data from treatments where either 0% or 100% of the surface area of the field is trafficked. These do not represent traffic patterns in commercial alfalfa production operations. To further understand the impact of field traffic on alfalfa yield, different percentages of traffic at harvest were analyzed. Our objectives were to quantify the yield produced from different intensities of harvest traffic throughout a 4-yr production cycle. The experimental units were furrow-irrigated raised bed systems with four harvests per year on a Youngston clay loam. A John Deere 2955, weighing 4004 kg, trafficked 0, 21, 42, or 83% of the area of alfalfa plots 7 d after swathing. The 0, 21, and 42 % trafficked treatments did not reduce yield in any year. The 83% trafficked alfalfa had 7 and 10% lower yields in the second and third years of production but had no effect the first and fourth years. The cumulative 4-yr yield from the 83% trafficked alfalfa was 7% lower than the 0% trafficked alfalfa. Single passes of a tractor impacting a high percentage of the field (83%) decreased yearly yield but was not detectable until the second year. Yield was the same whether the experimental units received 0 or 42% traffic.
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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".