Light Quality and the Critical Period for Weed Control in Soybean
Bibliographic record
Abstract
The critical period for weed control (CPWC) is an integral component of integrated weed management strategies. Several studies have defined the CPWC in soybean under varying agronomic conditions, yet none have described the mechanisms involved in crop yield losses caused by weed competition. We hypothesized that under nonresource-limiting conditions, morphological changes resulting from the expression of shade avoidance could be used to define a period of developmental sensitivity to low red-to-far-red ratio (R : FR) that would overlap with the defined CPWC in soybean. Two experiments (a sequential harvest and a weed addition/removal series) were conducted in 2008 and 2009 under controlled environmental conditions to address this hypothesis. Two light-quality treatments were used: (1) high R : FR ratio (i.e., weed-free), and (2) low R : FR ratio (i.e., weedy). The low R : FR ratio treatment induced shade avoidance responses in soybean, which included increases in height, internode length, and the shoot : root ratio, as well as a reduction in biomass accumulation and leaf number. Using the morphological changes in biomass and leaf number observed in the weed addition/removal series, a period of developmental sensitivity to low R : FR was defined between the first trifoliate (V1) and third trifoliate (V3) stages of soybean development. This period was found to be very similar to the CPWC previously defined by field studies of soybean–weed competition.
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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".