Detection of Neighboring Weeds Alters Soybean Seedling Roots and Nodulation
Bibliographic record
Abstract
Crop and weed competition studies rarely determine how plant-to-plant interactions alter the structure and physiology of crop roots. Soybean has the ability to detect neighboring weeds and to alter growth patterns including the allocation of resources to root growth. In this study, we hypothesized that low red : far red light ratio (R : FR) reflected from aboveground vegetative tissue of neighboring weeds would alter soybean root morphology and reduce root biomass and nodule number. All experiments were conducted under controlled conditions in which resources of light, water, and nutrients were nonlimiting. Low R : FR reflected from aboveground neighboring weeds reduced soybean seedling root length, surface area, and volume, including the number of nodules per plant. An accumulation of H 2 O 2 , an increase in malondialdehyde (MDA) content, a reduction in flavonoid content, and a decrease in 1,1-diphenyl-2-picrylhydrazyl (DPPH)–radicle scavenging activity were observed. The reduction in flavonoid content was accompanied by a decrease in the transcription of Gm IFS and Gm N93 and an increase in transcript levels of several antioxidant genes. These molecular and physiological changes may have a physiological cost to the soybean plant, which may limit the plant's ability to respond to subsequent abiotic and biotic stresses that will occur under field conditions.
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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) | 0.001 | 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".