Potential Pollen and Seed Production from Early- and Late-Emerging Common Ragweed in Corn and Soybean
Bibliographic record
Abstract
Despite the abundance of common ragweed in crops and the potency of ragweed pollen as an allergen, pollen production in agricultural fields has hardly been evaluated. Our goal was to evaluate pollen and seed production of early- (i.e., plants missed by weed control) and late- (i.e., after weed control) emerging common ragweed growing in corn and soybean. Allocation and gender distribution were also evaluated. The experiment included 2 yr (2008, 2009), three competition treatments, two seeding/emergence dates, three densities, and four replicates. Competition treatments (main plots) included no crop or weeds (bare), corn, or soybean. Crops were glyphosate resistant. Subplots were seeded with common ragweed before or after glyphosate application at densities of 1 (4 m−2), 3 (12 m−2), or 6 (24 m−2) plants per plot. Ragweed plants were harvested in mid-October and measured (aboveground biomass, length of all male inflorescences, stem diameter, and seed production). Based on our estimates, mean (backtransformed from ln[x+ 1]) pollen production values were: 6.25 (bare), 0.74 (corn), and 1.13 (soybean) × 108pollen grains per ragweed. Biomass and diameter were good predictors of ragweed male and female fitness. Plant height was not correlated with maleness. In crops, ragweed gender distribution was shifted toward maleness. Estimations indicate early-emerging (June 18 to 23) ragweed produced three times more pollen than late (July 7 to 11) plants.
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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) | 0.000 | 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".