Editorial: Integrative systems biology and big data for agricultural improvement and understanding
Bibliographic record
Abstract
Editorial on the Research Topic Integrative systems biology and big data for agricultural improvement and understandingIn the modern age, the agriculture industry faces the dual challenge of feeding an increasing global population and doing so sustainably.Meeting this challenge requires a more global understanding of biological systems.Integrative Systems Biology, a multidisciplinary approach that combines biology, genomics, and data analysis, has emerged as a powerful tool for improving agriculture and our comprehension of its biological components.Agriculture has come a long way since the days of subsistence farming.Today, it is a complex web of interconnected factors, where crop yields, climate change, and environmental sustainability intersect.To navigate this complexity, Integrative Systems Biology allows us to study the intricate relationships within ecosystems, from the molecular level up to the farm scale.One pivotal facet of this paradigm shift is the exploration of microbiomes-complex communities of microorganisms that play a fundamental role in shaping the health, productivity, and sustainability of agricultural ecosystems.This Research Topic delves into the realm of systems biology with a keen focus on microbiomes, leveraging high-throughput sequencing technologies to unravel the mysteries hidden within diverse agricultural domains.High-throughput sequencing, a revolutionary tool in genomics, has paved the way for a comprehensive exploration of microbial communities at unprecedented scales.This Research Topic embarks on an exploration journey across various agricultural landscapes, namely, forage systems, raw milk production, swine industry, and maple syrup production.Each of these fields represents a unique microcosm where the delicate balance of microbial interactions significantly influences the outcomes in terms of yield, quality, and overall system resilience.This Research Topic aims to bridge the gap between traditional agricultural practices and cutting-edge molecular technologies, emphasizing the importance of systems biology and high-throughput sequencing in shaping the future of sustainable and resilient agricultural systems.Specifically, here is a summary of the content covered by the different research papers:
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
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".