Barley and triticale underseeded with a kura clover living mulch: Effects on weed pressure, disease incidence, silage yield, and forage quality
Bibliographic record
Abstract
Kosinski, S. M., King, J. R., Harker, K. N., Turkington, T. K. and Spaner, D. 2011. Barley and triticale underseeded with a kura clover living mulch: Effects on weed pressure, disease incidence, silage yield, and forage quality. Can. J. Plant Sci. 91: 677–687. Our objective was to evaluate the effects of seeding barley (Hordeum vulgare L.) and triticale (×Triticosecale wittmack) into a living mulch of kura clover (Trifolium ambiguum M. Bieb.) on weed pressure, barley leaf disease levels, silage yield, and forage quality. Field studies were conducted at two locations in the Parkland region of Alberta, Canada, in 2006 and 2007. Barley and triticale were seeded, at three different soil nitrogen levels, into an established living mulch of kura clover that was either unsuppressed or chemically suppressed with glyphosate at 0.41 kg a.i. ha−1prior to cereal seeding. Barley and triticale sole crops were also included for comparison. The presence of the suppressed and unsuppressed kura clover living mulches significantly decreased weed pressure in 2007 and barley leaf disease incidence in 2006 and 2007. Silage DM yield of the cereal sole crops ranged from 5.18 to 7.02 Mg ha−1, compared with yields of 4.57 to 5.21 Mg ha−1from the unsuppressed living mulch treatments and from 3.95 to 6.00 Mg ha−1from the suppressed living mulch treatments. The kura clover increased the relative feed value of the silage. Based on these results, there is the potential for a suppressed kura clover living mulch to be utilized for cereal silage production in Alberta.
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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 | 0.001 |
| 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".