Does the shade avoidance response contribute to the critical period for weed control in maize (<i>Zea mays</i>)?
Bibliographic record
Abstract
Summary The effect of early weed emergence on crop seedling development has not been analysed within the context of a critical period study. Experiments were conducted to quantify the influence of a low light quality environment (i.e., low ratio of reflected red to far‐red light (R/FR)) on maize seedling growth and development under non‐limiting resource conditions. Weed‐addition and ‐removal series were constructed, such that the effects of R/FR on seedling growth and development were isolated from those of direct competition. Maize seedlings responded to the presence of weeds within 24 h of addition by increasing plant height, which was followed by a subsequent reduction in the rate of leaf appearance. Seedling biomass and leaf area decreased linearly in the weed removal series with increasing duration of weed presence. Conversely, seedlings in the weed‐addition series were unaffected. These results demonstrate that early exposure to weeds reduced the rate of seedling growth and development and that this effect was most pronounced if it was initiated upon emergence. This suggests that the existence of a period of developmental sensitivity to R/FR precedes the defined critical period for weed control in maize. These early physiological changes triggered by the R/FR ratio may contribute to the onset of the critical time of weed removal.
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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".