Mechanisms of Floral Induction in Grasses: Something Borrowed, Something New
Bibliographic record
Abstract
Almost all that is known about the transition to flowering in grasses is based on studies of agronomic species. The grain produced by two tropically derived grasses, maize (Zea mays) and rice (Oryza sativa), and a temperate origin grass, wheat (Triticum aestivum), provides most of the world's food. Other grasses, such as barley (Hordeum vulgare), ryegrass species (Lolium spp.), sorghum (Sorghum bicolor), and oats (Avena sativa), are grown in lesser amounts, but also fill important food production niches. In grass species, such as sugarcane (Saccharum spp.), the vegetative portion of the plant is harvested for the Suc that accumulates in its stalks; in this crop, the inability to flower is desirable because sugar levels drop after plants make the transition to flowering as carbon assimilates are shunted to seed production. In all of these grasses, manipulation of the timing of the floral transition is a vitally important trait in maximizing yield potential. Extensive agronomic studies have been done on grass species, but studies of the small flowering dicot plant Arabidopsis (Arabidopsis thaliana) have provided an abundance of information on the genetic and molecular control of flowering. What has emerged is a complex network of genes and pathways, some parts of which are also found in the grasses. Conversely, recent discoveries show that grasses also have developed unique mechanisms to regulate flowering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".