Red–far-red ratio of reflected light: a hypothesis of why early-season weed control is important in corn
Bibliographic record
Abstract
A plant's ability to detect and adjust morphologically to changes in light quality (red–far-red [R:FR] ratio) is one mechanism by which a crop plant responds to weeds. To test this hypothesis, two experiments were conducted where corn was grown in growth cabinets under different light environments. First, to determine the effect of R:FR ratio on corn growth and development, treatments of high R:FR (1.37) and low R:FR (0.67) ratio were compared. These were established by planting corn in pots and then placing trays of either turface (a baked clay medium with high R:FR) or commercial grass sod (low R:FR) on each side of a row of corn pots. Grass sod was used to simulate low-growing weeds. The low R:FR sod treatment resulted in corn plants which were taller, had larger leaves, and greater shoot–root ratio than plants growing in the high R:FR turface treatment. In the second experiment, the effect of R:FR ratio on corn leaf azimuth position was examined. This was accomplished by adding a third treatment where each corn row had sod placed on one side and turface on the other. The proportion of leaves in four azimuthal classes was recorded. In the presence of sod, the proportion of leaves perpendicular to the corn row decreased, and this altered the proportion of leaves in other classes. Therefore, corn seedlings detected changes in light quality caused by the presence of sod (which simulated low-growing weeds) and responded by adjusting carbon allocation and leaf orientation to optimize the interception of light quantity and quality. These results support our hypothesis that low-lying vegetation can alter the growth of corn seedlings before competition for resources occurs. This change in growth may help explain the importance of early-season weed control in corn.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".