Merging traditional practices and modern technology through computational plant breeding
Bibliographic record
Abstract
Plant breeding has transitioned from its ancient agrarian roots to a modern, sophisticated discipline blending advanced genetic and computational techniques. Initially led by intuition and basic selection, the field was revolutionized in the 19th century by Gregor Mendel's principles. Today, plant breeding utilizes multiomics approaches and data science techniques to navigate vast amounts of data and deepen our understanding of the biological mechanisms behind specific traits. To tackle the challenges of big data, the discipline now incorporates computational biology, data science, and bioinformatics, which have become integral to routine plant breeding practices. As plant breeders have explored these promising fields, many have adopted titles such as "plant breeder and computational biologist" or "plant breeder and bioinformatician." However, these titles may lead to misconceptions about expertise, as breeders often apply a blend of these skills without specializing fully in each domain. Recognizing this, it is crucial to establish a clear identity for the evolving skill set of modern plant breeders. In this review, I explore the historical evolution of plant breeding, highlighting the transformative role of computational biology. Furthermore, I address the potential pitfalls of adding titles to plant breeding and propose the adoption of the term "computational plant breeding." This term more accurately reflects the integrated application of computational tools and biological insights in plant breeding. By redefining this emerging field, we can better appreciate its unique contributions and prepare for future advancements in agricultural science.
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 | 0.006 | 0.007 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".