Identifying Critical Control Points in the Wild Oat (<i>Avena fatua</i>) Life Cycle and the Potential Effects of Harvest Weed-Seed Control
Bibliographic record
Abstract
Wild oat is a problematic weed species that requires new management techniques in the face of herbicide resistance; harvest weed-seed control (HWSC) may be an option. Wild oat demographic information was collected in long-term, rotational field studies in Lacombe, AB, Canada, in 2006 and 2007, and a periodic matrix model was parameterized using management extremes (no IPM, no herbicide to high IPM, and full herbicide). Population growth rates were calculated for each treatment and year. Prospective (elasticity) and retrospective (LTRE) analyses were conducted alongside a rearrangement of the model equation in which population growth rates were designated and the required proportion of newly shed seed survival that gives that growth rate was solved for. All populations had λ > 1 or increasing populations. Elasticity analyses indicated that λ was most-highly elastic to the overwinter seedbank (Esw = 1), followed by seedling survival, fecundity, and survival of newly shed seed (0.63 to 0.86 across treatments). The latter may be the most-accessible vital rate for management of herbicide resistant populations. LTRE exposed the stochasticity of wild oat population growth rates between years and their ability to take advantage of lapses in control. Decreasing the proportion of newly shed seeds (snew) that survives was the most-effective and available control strategy until reduced to 0.1 to 0.3 when the summer seedbank becomes more critical. When averaged across treatments, > 80% of newly shed seed must be eliminated to stop the population from growing, resulting in a stable population, but not a decline. Because of preharvest shattering, HWSC will likely not be effective enough alone to cause wild oat populations to decline. New management techniques for wild oat control that can be used in combination with HWSC and integrated weed management strategies are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".