The impact of underseeding forage mixtures on barley grain production in northern North America
Bibliographic record
Abstract
Livestock farmers in Newfoundland grow most of their required forage, yet must import most feed grain. Growing barley (Hordeum vulgare L.) in the year of forage establishment may allow for the incorporation of grain production into local cropping schemes. We examined the effect of barley grain production over an establishing timothy (Phleum pratense L.)-clover (Trifolium pratense L.; T. hybridum L.) forage sward in a 4-yr study near St. John’s. The experiment compared two barley varieties (differing in plant height), three barley seeding rates and the effect of a forage under-story on grain production in the establishment year, and forage production in the subsequent year. Increasing barley seeding rate from 125 to 375 plants m-2 resulted in a linear increase in spikes m-2, which led to a linear increase in barley yield. Pure-stand grain yields did not differ from those undersown to forage mixtures. The production of barley grain in the establishment year did not alter forage yield in the subsequent year (at any barley seeding rate or cultivar archetype). The barley crop did alter forage species composition in that higher seeding rates resulted in 15% less timothy in the forage production year. Barley undersown at a rate of 375 seeds m-2 with a timothy-clover mixture can be produced successfully in Newfoundland. Key words: Hordeum vulgare L., alsike clover, red clover, underseeding, companion planting, Newfoundland
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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".