Diverse Rotations and Optimal Cultural Practices Control Wild Oat (<i>Avena fatua</i>)
Bibliographic record
Abstract
In western Canada, more money is spent on wild oat herbicides than on any other weed species, and wild oat resistance to herbicides is the most widespread resistance issue. A direct-seeded field experiment was conducted from 2010 to 2014 at eight Canadian sites to determine crop life cycle, crop species, crop seeding rate, crop usage, and herbicide rate combination effects on wild oat management and canola yield. Combining 2× seeding rates of early-cut barley silage with 2× seeding rates of winter cereals and excluding wild oat herbicides for 3 of 5 yr (2011 to 2013) often led to similar wild oat density, aboveground wild oat biomass, wild oat seed density in the soil, and canola yield as a repeated canola–wheat rotation under a full wild oat herbicide rate regime. Wild oat was similarly well managed after 3 yr of perennial alfalfa without wild oat herbicides. Forgoing wild oat herbicides in only 2 of 5 yr from exclusively summer annual crop rotations resulted in higher wild oat density, biomass, and seed banks. Management systems that effectively combine diverse and optimal cultural practices against weeds, and limit herbicide use, reduce selection pressure for weed resistance to herbicides and prolong the utility of threatened herbicide tools.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".