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.
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 | 0.000 | 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".