Genotype by Environment Interaction Unravels Influence on Secondary Metabolite Quality in Cassava Infested by Bemisia tabaci
Bibliographic record
Abstract
Cassava resistance to Bemisia tabaci is a result of many plant processes which involve plant biochemical constituents, shown to be affected by genotype and environment. The objective of this study was to assess the effect of genotype × environment interactions on concentrations of tannin, flavonoid, total phenolic content, antioxidative capacity and B. tabaci resistance. Fifteen cassava genotypes were evaluated monthly for tannin, flavonoid, total phenolic content and antioxidative capacity in three locations over two seasons with varying temperatures and rainfall. In addition, data were collected on B. tabaci population density and damage. The data collected was subjected to analysis of variance and additive main effects and multiplicative interactions (AMMI) analyses. Flavonoid, total phenolic content and antioxidative capacity varied significantly (P < 0.001) across seasons with higher concentrations in season one than season two, attributed to different temperature and rainfall readings. Total phenolic content was significantly (P < 0.001) associated to antioxidative capacity (r = 0.83) and temperature (r = 0.91). Leaf damage due to adult whitefly and nymphs was significantly (P < 0.001) negatively correlated (r = -0.67) to antioxidative capacity. Genotypes UG 120257, UG 120291 and UG 120124 were shown to have high antioxidative capacity and more stable performance across environments. Temperature and B. tabaci feeding influenced concentrations of the phenolic content and antioxidative activity, as a result affected cassava resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".