An efficient method for the physical mapping of transgenes in barley using in situ hybridization
Bibliographic record
Abstract
The genetic transformation of crops by particle bombardment and Agrobacterium tumefaciens systems have the potential to complement conventional plant breeding programmes. However, before deployment, transgenic plants need to be characterized in detail, and physical mapping is an integral part of this process. Therefore, it is important to have a highly efficient method for transgene detection by fluorescence in situ hybridization (FISH). This study describes a new approach, which provides efficient control of probe length and labelling, both of which play an important role in in situ hybridization of transgenes. The approach is based on reducing the size of the plasmid prior to labelling by nick translation, rather than using the whole or linearized plasmid, or varying the amounts of DNaseI in the nick translation mixture. This provided much more efficient labelling of the probe, which yielded optimal hybridization. minimal fluorescent background, and accurate physical location of the transgene.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".