Wild Oat (<i>Avena fatua</i>) vs. Redstem Filaree (<i>Erodium cicutarium</i>) Interference in Dry Pea
Bibliographic record
Abstract
Dry peas (pea) usually require early and effective weed management for optimum yields. However, it is not always possible to control all weeds with a single herbicide application. In experiments at Lacombe and Lethbridge, Alberta, Canada, we determined the relative importance of controlling redstem filaree or wild oat, or both species. Bentazon, sethoxydim, or a imazethapyr/imazamox mixture was applied to control redstem filaree, wild oat, or both weeds, respectively. None of the herbicides caused visually detectable crop injury. Time of weed removal effects on pea yield were inconsistent. In addition, applying half or full herbicide rates did not usually influence weed biomass, pea yield, or pea seed weight. Averaged across all variables except herbicide, pea yield losses due to competition from redstem filaree, wild oat, or both species averaged 31, 47, or 53%, respectively. When redstem filaree and wild oat were controlled with imazethapyr/imazamox, pea yields were the same as weed-free check plots in three of four location-years (89% of weed-free yields for all four location-years). Optimal pea yields in weed communities with redstem filaree and wild oat as dominant species were more dependent upon selecting an herbicide that controlled both species than a specific time of weed removal or herbicide rate.
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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".