Referee report. For: High transformation efficiency in Arabidopsis using extremely low Agrobacterium inoculum [version 1; peer review: 1 approved, 1 approved with reservations]
Bibliographic record
Abstract
Agrobacterium -mediated transformation methods have allowed the stable introduction of target genes into the nuclear genomes of recipient plants. Among them, the floral dip approach represents the simplest due to its straightforwardness and high transformation efficiency. In a standard floral dip protocol that most researchers follow, Agrobacterium cells are grown to stationary phase (OD 600 ≈2.0) in large cultures and resuspended in inoculation medium to OD 600 ≥0.8. Here, we tested the effects of low Agrobacterium inoculum on transformation rate. Our data revealed that the floral dip method still guarantees relatively high transformation rate in Arabidopsis thaliana Col-0 ecotype even with very low Agrobacterium inoculum (OD 600 =0.002). Our finding thus simplifies the floral dipping protocol further, which allows transformation with small bacterial culture and enables high-throughput transformation of large numbers of constructs in parallel.
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.540 | 0.332 |
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".