Lower levels of harvest traffic on alfalfa (<i>Medicago sativa</i>L.) have minimal impact on long-term yields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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 it