New resources for studying the rose flowering process
Bibliographic record
Abstract
Knowledge of the flowering process - an important trait in ornamental plants such as roses - is necessary for efficient control of flowering. This study was carried out to develop and characterize new resources to gain further insight into floral control in rose. We studied floral initiation in a nonrecurrent blooming rose (hybrid of Rosa wichurana) and a recurrent blooming rose (Rosa hybrida Black Baccara. In Black Baccara, floral initiation took place rapidly after bud burst, whereas in the greenhouse R. wichurana remained vegetative. During floral initiation, the apex enlarged and domed quickly and concomitantly. This is the first description of this transition between the vegetative and floral bud stages in rose. From these vegetative and pre-floral tissues, two cDNA libraries were constructed and 5,000 ESTs sequenced. By collecting our ESTs and those available in public databases, we developed a comprehensive database representing approximately 5,000 unique sequences after clustering. By screening this database for candidate genes involved in the flowering process, we identified 13 genes potentially involved in gibberellic acid signalling, photoperiod pathways, and floral development. Based on expression data, we put forward different hypotheses on the control of flowering in rose (photoperiod control and involvement of gibberellins) relative to what is already known in Arabidopsis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".