Investigation of Metabolic Resistance to Soybean Aphid (Aphis glycines Matsumura) Feeding in Soybean Cultivars
Bibliographic record
Abstract
) herbivore pest in many soybean growing regions. High numbers of aphids on soybean can cause severe reductions in yield. The management of soybean aphids includes monitoring, insecticide applications when required, and the use of resistant cultivars. Soybean aphid-resistant soybean varieties are associated with genes that confer one or more categories of resistance to soybean aphids, including antibiosis (affects survival, growth, and fecundity), antixenosis (affects behaviour such as feeding), and tolerance (plant can withstand greater damage without economic loss). The genetic resistance of soybean to several herbivores has been associated with isoflavonoid phytoalexins; however, this correlation has not been observed in soybean varieties commonly grown in southern Ontario, Canada. Isoflavonoids in the leaves of 18 cultivars in the early growth stage were analyzed by HPLC and the concentration by fresh weight was used to rate the potential resistance to aphids. Greenhouse and growth cabinet trials determined that the cultivars with greater resistance to aphids were Harosoy 63 and OAC Avatar. The most susceptible cultivar was Maple Arrow, whereas Pagoda and Conrad were more tolerant to aphid feeding damage. Overall, there was a low correlation between the number of aphids per leaf, feeding damage, and leaf isoflavonoid levels. Metabolite profiling by high-resolution LC-MS determined that the most resistant cultivar had on average lower levels of certain free amino acids (Met, Tyr, and His) relative to the most susceptible cultivar. This suggests that within the tested cultivars, nutritional quality stimulates aphid feeding more than isoflavonoids negatively affect aphid feeding or growth. These findings provide a better understanding of soybean host plant resistance and suggest ways to improve soybean resistance to aphid feeding through the breeding or metabolic engineering of leaf metabolites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".