Genotypic variation in the response of chickpea to arbuscular mycorrhizal fungi and non-mycorrhizal fungal endophytes
Bibliographic record
Abstract
Plant roots host symbiotic arbuscular mycorrhizal (AM) fungi and other fungal endophytes that can impact plant growth and health. The impact of microbial interactions in roots may depend on the genetic properties of the host plant and its interactions with root-associated fungi. We conducted a controlled condition experiment to investigate the effect of several chickpea (Cicer arietinum L.) genotypes on the efficiency of the symbiosis with AM fungi and non-AM fungal endophytes. Whereas the AM symbiosis increased the biomass of most of the chickpea cultivars, inoculation with non-AM fungal endophytes had a neutral effect. The chickpea cultivars responded differently to co-inoculation with AM fungi and non-AM fungal endophytes. Co-inoculation had additive effects on the biomass of some cultivars (CDC Corrine, CDC Anna, and CDC Cory), but non-AM fungal endophytes reduced the positive effect of AM fungi on Amit and CDC Vanguard. This study demonstrated that the response of plant genotypes to an AM symbiosis can be modified by the simultaneous colonization of the roots by non-AM fungal endophytes. Intraspecific variations in the response of chickpea to AM fungi and non-AM fungal endophytes indicate that the selection of suitable genotypes may improve the ability of crop plants to take advantage of soil ecosystem services.
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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 | 0.001 | 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) | 0.001 | 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".