Shade avoidance: an integral component of crop–weed competition
Bibliographic record
Abstract
PageER, TollenaarM, LeeEA, LukensL & SwantonCJ (2010). Shade avoidance: an integral component of crop–weed competition.Weed Research50, 281–288. Summary Crop–weed competition is comprised of both resource dependent and resource independent processes. While many studies have focused on the role that resource dependant competition plays in reducing crop yields, few have investigated whether resource independent effects may contribute to these losses. In this study, we identify the red‐to‐far‐red ratio as a variable that contributing to resource independent competition and tested the hypothesis that the expression of shade avoidance in response to weeds reduces maize fitness (i.e., kernel number) in the absence of resource dependent competition. Seedlings were grown in a field fertigation system under two light quality environments: an ambient and a low red‐to‐far‐red ratio environment, which were designed to simulate weed‐free and weedy conditions respectively. Plants that expressed classic shade avoidance characteristics set fewer kernels per plant and partitioned less biomass to the developing ear. Shade avoidance also doubled the plant‐to‐plant variability in these yield parameters (i.e., kernel number and harvest index) without affecting the mean or frequency distribution of shoot biomass at maturity. We propose that shade avoidance should be viewed as an integral component of the process of competition. This resource independent response precedes and conditions the crop seedling for the onset of resource dependent 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".