Pre-Screening of Banana Genotypes for Fusarium Wilt Resistance by Using an In Vitro Bioassay
Bibliographic record
Abstract
Abstract In the process of breeding and selection of banana for resistance to Fusarium wilt, it is important to conduct an efficient resistance screening test by artificial inoculation withFusarium oxysporumf. sp.cubense(Foc) Tropical Race 4. So far, there are two types of early bioassays for screeningMusagenotypes against Foc: a greenhouse and anin vitrobioassay. The most commonly used greenhouse bioassay is a pot-based system followed by a hydroponic system. Here we describe anin vitrobioassay characterized byin vitroinoculation of rooted banana plantlets grown on medium consisting of half-strength MS macronutrients and MS micronutrients. The disease response and evaluation results obtained through thisin vitrobioassay correlates with that from a greenhouse screen and/or field evaluation. Given the importance ofin vitrocell and tissue culture techniques for banana (mutation) breeding, promising resistant clones could be screened directly. Thisin vitrobioassay is a totally contained system compared with greenhouse methods and does not require an acclimatization step, thereby improving banana breeding efficiency. Thein vitropre-screening protocol and bioassay for Fusarium wilt resistance presented here is fast, space-effective, and accurate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".