The floral transcriptome of <i><scp>E</scp>ucalyptus grandis</i>
Bibliographic record
Abstract
As a step toward functional annotation of genes required for floral initiation and development within the Eucalyptus genome, we used short read sequencing to analyze transcriptomes of floral buds from early and late developmental stages, and compared these with transcriptomes of diverse vegetative tissues, including leaves, roots, and stems. A subset of 4807 genes (13% of protein-coding genes) were differentially expressed between floral buds of either stage and vegetative tissues. A similar proportion of genes were differentially expressed among all tissues. A total of 479 genes were differentially expressed between early and late stages of floral development. Gene function enrichment identified 158 gene ontology classes that were overrepresented in floral tissues, including 'pollen development' and 'aromatic compound biosynthetic process'. At least 40 floral-dominant genes lacked functional annotations and thus may be novel floral transcripts. We analyzed several genes and gene families in depth, including 49 putative biomarkers of floral development, the MADS-box transcription factors, 'S-domain'-receptor-like kinases, and selected gene family members with phosphatidylethanolamine-binding protein domains. Expanded MADS-box gene subfamilies in Eucalyptus grandis included SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1), SEPALLATA (SEP) and SHORT VEGETATIVE PHASE (SVP) Arabidopsis thaliana homologs. These data provide a rich resource for functional and evolutionary analysis of genes controlling eucalypt floral development, and new tools for breeding and biotechnology.
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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".