Applied Metabolomics for Plant Biotechnology
Bibliographic record
Abstract
This chapter explores the dynamic field of applied metabolomics in the context of plant biotechnology. Metabolomics, the systematic study of small molecules, offers valuable insights into the intricate biochemical processes within plant cells. The chapter begins by elucidating the fundamental principles of metabolomics and its applications, emphasizing its pivotal role in unraveling the complex metabolic networks governing plant growth, development, and the response to environmental stimuli. A comprehensive overview of cutting-edge analytical techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, is provided. The chapter further delves into case studies and success stories, showcasing how metabolomics has been instrumental in addressing key challenges in plant biotechnology, such as enhancing crop yield, stress tolerance, and the production of bioactive compounds. Moreover, it discusses the integration of metabolomics with other omics approaches, fostering a holistic understanding of plant systems. As plant biotechnology continues to evolve, the role of metabolomics as a powerful tool for precision agriculture and sustainable crop improvement will become increasingly evident. This chapter serves as a valuable resource for researchers, students, and practitioners seeking to harness the potential of metabolomics in advancing plant biotechnology.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.025 |
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".