Sustainable agriculture and the multigenomic model: how advances in the genetics of arbuscular mycorrhizal fungi will change soil management practices.
Bibliographic record
Abstract
Arbuscular mycorrhizal (AM) fungi are important both in agriculture and in natural ecosystems due to their effects on the fitness of their plant hosts. As symbionts, AM fungi improve plant uptake of water and nutrients, and help to protect against pathogens. The study of these organisms has been obstructed in part by difficulty in identifying and quantifying them in the field, a problem springing from our poor understanding of their unusual genetic structure. AM fungi have been shown to be multigenomic, possessing a large amount of genetic variation not only between individuals, but among nuclei within an individual. In order for simple, reliable identification and quantification techniques to be developed for large-scale use, this genetic diversity must be quantified and marker genes found for which the amount of variation is manageable. Once this has been accomplished, growers can work knowledgeably with the existing strains of AM fungi in their fields, or select an appropriate commercial inoculum. In this chapter, we will discuss the current state of knowledge in AM fungal genetics and how it can be applied to develop molecular tools which permit the management of natural AM fungal populations in agricultural fields. Assessment of AM fungal biodiversity in natural and modified ecosystems, as well as the estimation of the mycorrhizal potential of agricultural soil, are bottlenecks that greatly limit our understanding of the ecology of these cornerstone organisms. The development of efficient and inexpensive diagnostic techniques will enable us to use them to their full potential in sustainable agriculture systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".