Characterization of Emergence Flows of Volunteer Corn as Function of the Type of Harvest Grain Loss
Bibliographic record
Abstract
Volunteer corn resistant to glyphosate is constant as weed in soybeans planted in succession. This work aimed to identify the emergence flows of volunteer corn plants in the period of time from the harvest of corn planted following soybean (autumn corn) to the plenty establishment of the canopy of the following soybean crop (summer soybean), as a function of different types of propagules generated by preceding corn harvest losses. Four field experiments were carried out in 2013 and 2014, at a Cerrado location (Sinop, MT) and a subtropical location (Londrina, PR), Brazil. Treatments included the distribution of corn crop residues (factor “A”) either on soil surface or incorporated into superficial soil layers. Four types of propagules (factor “B”) were characterized as ears with whole husk; ears with half husk; broken ears, no husk; and loose grains. The density of emerged plants was recorded fortnightly between August and December. When partially incorporated into soil, propagules generate an increased density of emerged plants as compared to the surface deposition treatments. The main sources of volunteer plants, in descending order of importance, were: (1) loose grains, (2) broken ears, (3) ears with damaged husk, and (4) ears with intact husk. Ears emerged later compared to loose grains or broken ears. Climatic conditions influence the emergence pattern. For the climate of (Savannah-like) Cerrado, the beginning of the rainy season is preponderant for the start of corn emergence. Under subtropical climatic conditions, mild low temperatures, associated or not to rainfall, determine the emergence peaks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".