Microbiome: diversity, distribution, and potential role in sustainable crop production
Bibliographic record
Abstract
From microbiomes in cheese to soil, many organisms provide enhancement, enrichment, production, and some degradation to the niche they occupy. Although microbial diversity and microbiomes have been known for centuries, it has become a trendy area to investigate with the advent of omics approaches where a vast amount of information can be gathered about the nature and befits of these microorganisms. This information is crucial given the world’s population rise. According to FAO, feeding the world’s 9.1 billion people in 2050 will necessitate a 70 percent increase in overall food production. Five steps have been offered to fulfil this increasing global food supply-demand, including decreasing agriculture’s footprint, growing more on existing farms, discovering ways to use resources efficiently, modifying our diets, and reducing waste. It is essential to focus on management practices that influence soil health, sustainable productivity, and inorganic inputs to reduce agriculture’s footprint. Conventional agriculture has increased agricultural production on a large scale; however, overuse of chemicals has led to food contamination, negative environmental consequences, and disease resistance, all of which have important implications for human health and food security. Microbiome research makes it possible to simultaneously reduce these environmental impacts and steadily increase the quantity and quality of agricultural products. Plant microbiome research focuses on defining the structure and activities of microbial communities in various plant-associated habitats and establishing a relationship between specific microbial taxa and plant performance. The microbes associated with plants in the plant microbiome can provide a variety of advantages, including plant growth promotion, soil fertility and control of pests and plant pathogens. Traditional approaches can only examine a small percentage of the vast unexplored soil microbial world because of their biased detection of microbial genetics and functional diversity through culture methods. Today, with the advent of highthroughput sequencing techniques and a variety of “omics” approaches, researchers can now identify microbiome structure and dynamics along with host interactions on an extraordinary level. These approaches will be valuable in establishing the relationship between the structure and function of the soil microbial community and gaining a better understanding of environmental and ecological processes, with a focus on plantmicrobe ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".