Wheat Seeding Rate Influences Herbicide Performance in Wild Oat (<i>Avena fatua L.</i>)
Bibliographic record
Abstract
Field experiments were conducted at three locations in Alberta for 3 yr to determine if spring wheat (Triticum aestivum L.) seeding rate (75 and 150 kg ha−1) influenced the effects of recommended and reduced herbicide rates on wild oat (Avena fatua L.) shoot biomass, wild oat seed in the soil seed bank, and wheat yield and net economic return. Wild oat biomass and seed in the soil seed bank decreased nonlinearly at both seeding rates as herbicide rates increased. The herbicides were more effective in reducing wild oat shoot biomass and seed in the soil seed bank when wheat was seeded at the higher rate. The lowest wheat yields and net economic returns occurred when no herbicides were applied and both variables increased nonlinearly with increasing herbicide rate. In most cases, wheat yield and net economic return were greater at the higher seeding rate. On average, wheat yield improved by 19% and net economic return by 16% when wheat was seeded at the higher rate. The results indicate that seeding wheat at relatively high rates can contribute positively to herbicide performance and result in better wild oat management and higher wheat yields and economic returns. In some cases, there was little difference between applying the herbicides at 75 or 100% of the recommended rate but reducing rates below 75% almost always resulted in higher wild oat shoot biomass and seed, and reduced yields and net economic returns, even at the higher wheat seeding rate.
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.
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".