An efficient field screening procedure for identifying transposants for constructing an <i>Ac</i>/<i>Ds</i>-based insertional-mutant library of rice
Bibliographic record
Abstract
An efficient system was developed, and several variables tested, for generating a large-scale insertional-mutagenesis population of rice. The most important feature in this improved Ac/Ds tagging system is that one can conveniently carry out large-scale screening in the field and select transposants at the seedling stage. Rice was transformed with a plasmid that includes a Basta-resistance gene (bar). After the Ds element is excised during transposition, bar becomes adjacent to the ubiquitin promoter, and the rice plant becomes resistant to the herbicide Basta. In principle, one can plant up to one million plants in the field and select those plants that survive after spraying with Basta. To test the utility of this system, 4 Ds starter lines were crossed with 14 different Ac plants, and many transposants were successfully identified after planting 134,285 F2 plants in the field. Over 2,800 of these transposants were randomly chosen for PCR analysis, and the results fully confirmed the reliability of the field screening procedure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".