Both Promoters and Inhibitors Affected Flowering Time in Grafted Soybean Flowering‐Time Isolines
Bibliographic record
Abstract
Understanding the control of flowering time in photoperiod‐sensitive plants has been furthered by grafting experiments. In soybean [Glycine max (L.) Merr.], genes which control flowering time have been identified and their responses to photoperiod characterized. Grafting experiments allow the study of interactions between genotypes. The objective of our study was to characterize the flowering response of scions from a grafted series of early‐ to late‐flowering soybean near‐isogenic lines. Seedling scions were grafted to 1‐wk‐older rootstocks and grown under noninductive 16‐h days. Rootstocks were allowed to develop a single axillary shoot to allow interaction between rootstock and scion shoots. Late‐flowering rootstocks did not delay flowering of the earliest‐flowering isolines but delayed flowering of intermediate‐flowering isolines. Some floral inhibition was also seen within scions since defoliated late‐flowering scions grafted to early‐flowering rootstocks flowered earlier compared with nondefoliated scions of the same graft combination. Early‐flowering rootstocks promoted flowering of late‐flowering scions both within and across genetic backgrounds. Early‐flowering scions flowered early (27 to 30 d) regardless of rootstock genotype. This early flowering was observed even when the scions were defoliated, indicating that floral promoters might be produced or sensed in unexpanded leaves or buds. The activity of floral promoters and inhibitors was demonstrated in soybean and these factors appeared to mediate flowering time antagonistically.
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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 | 0.001 |
| 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".