Exploring tomato Solanum pennellii introgression lines for residual biomass and enzymatic digestibility traits
Bibliographic record
Abstract
BACKGROUND: Residual biomass production for fuel conversion represents a unique opportunity to avoid concerns about compromising food supply by using dedicated feedstock crops. Developing tomato varieties suitable for both food consumption and fuel conversion requires the establishment of new selection methods. RESULTS: A tomato Solanum pennellii introgression population was assessed for fruit yield, biomass phenotypic diversity, and for saccharification potential. Introgression lines 2-5, 2-6, 6-3, 7-2, 10-2 and 12-4 showed the best combination of fruit and residual biomass production. Lignin, cellulose, hemicellulose content and saccharification rate showed a wide variation in the tested lines. Within hemicellulose, xylose value was high in IL 6-3, IL 7-2 and IL 6-2, whereas arabinose showed a low content in IL 10-2, IL 6-3 and IL 2-6. The latter line showed also the highest ethanol potential production. Alkali pre-treatment resulted in the highest values of saccharification in most of lines tested, suggesting that chemical pretreatment is an important factor for improving biomass processability. Interestingly, extreme genotypes for more than one single trait were found, allowing the identification of better genotypes. Cell wall related genes mapping in genomic regions involved into tomato biomass production and digestibility variation highlighted potential candidate genes. Molecular expression profile of few of them provided useful information about challenged pathways. CONCLUSIONS: The screening of S. pennellii introgression population resulted very useful for delving into complex traits such as biomass production and digestibility. The extreme genotypes identified could be fruitfully employed for both genetic studies and breeding.
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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) | 0.002 | 0.001 |
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".