A proposed model for the flowering signaling pathway of sugarcane under photoperiodic control
Bibliographic record
Abstract
Molecular analysis of floral induction in Arabidopsis has identified several flowering time genes related to 4 response networks defined by the autonomous, gibberellin, photoperiod, and vernalization pathways. Although grass flowering processes include ancestral functions shared by both mono- and dicots, they have developed their own mechanisms to transmit floral induction signals. Despite its high production capacity and its important role in biofuel production, almost no information is available about the flowering process in sugarcane. We searched the Sugarcane Expressed Sequence Tags database to look for elements of the flowering signaling pathway under photoperiodic control. Sequences showing significant similarity to flowering time genes of other species were clustered, annotated, and analyzed for conserved domains. Multiple alignments comparing the sequences found in the sugarcane database and those from other species were performed and their phylogenetic relationship assessed using the MEGA 4.0 software. Electronic Northerns were run with Cluster and TreeView programs, allowing us to identify putative members of the photoperiod-controlled flowering pathway of sugarcane.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".