Mineral Biofortification and Growth Stimulation of Lentil Plants Inoculated with Trichoderma Strains and Metabolites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biofortification of crops via agricultural interventions represents an excellent way to supply micronutrients in poor rural populations, who highly suffer from these deficiencies. Soil microbes can directly influence plant growth and productivity, e.g., by contrasting plant pathogens or facilitating micronutrient assimilation in harvested crop-food products. Among these microbial communities, Trichoderma fungi are well-known examples of plant symbionts widely used in agriculture as biofertilizers or biocontrol agents. In this work, eleven Trichoderma strains and/or their bioactive metabolites (BAMs) were applied to lentil plants to evaluate their effects on plant growth and mineral content in greenhouse or field experiments. Our results indicated that, depending upon the different combinations of fungal strain and/or BAM, the mode of treatment (seed and/or watering), as well as the supplementary watering with solutions of iron (Fe) and zinc (Zn), the mineral absorption was differentially affected in treated plants compared with the water controls. In greenhouse conditions, the largest increase in Fe and Zn contents occurred when the compounds were applied to the seeds and the strains (in particular, T. afroharzianum T22, T. harzianum TH1, and T. virens GV41) to the soil. In field experiments, Fe and Zn contents increased in plants treated with T. asperellum strain KV906 or the hydrophobin HYTLO1 compared with controls. Both selected fungal strains and BAMs applications improved seed germination and crop yield. This biotechnology may represent an important challenge for natural biofortification of crops, thus reducing the risk of nutrient deficiencies.
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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 | 0.000 |
| 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) | 0.000 | 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 it