Eight cycles of half-sib family recurrent selection to improve rubber yield in Russian dandelion
Bibliographic record
Abstract
Russian dandelion (Taraxacum kok-saghyz) produces natural rubber which can complement the world’s supply of this strategic commodity, derived mainly from the Para rubber tree (Hevea brasiliensis). Four cycles of half-sib family recurrent selection conducted previously in Russian dandelion improved rubber yield nearly 50%. The objectives of this research were to continue selection for four additional cycles and evaluate progress from eight generations of breeding, and assess the potential for future gains. Rubber yield increased from 0.205 to 0.378 g/plant from Cycle 0 (C0) to C8, representing an 84% improvement, or 10% per cycle. Increases from C0 to C4 were similar to those from C4 to C8. Root dry weight did not increase from selection and improved yield only resulted from enhancing rubber percentage which changed from 4.35% to 7.62%. Selection response has not plateaued, and phenotypic variation has not decreased, indicating continued gains can occur with additional breeding.
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 | 0.000 | 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".