Phenotypic limits of crop diversity: a data exploration of functional trait space
Bibliographic record
Abstract
Relationships between crop genetic and functional diversity are key to addressing contemporary agricultural challenges. Yet, there are few approaches for quantifying the relationship between genetic diversity and crop functional trait expression. Here, we introduce 'functional space accumulation curves' to analyze how trait space increases with the number of crop genotypes within a species. We explore the potential for functional space accumulating curves to quantify genotype-trait space relationships in four common annual crop species: barley (Hordeum vulgare), rice (Oryza sativa), soybean (Glycine max), and durum wheat (Triticum durum). We also employ these curves to describe genotype-trait space relationships in the wild annual Arabidopsis thaliana, which has not been subjected to artificial selection. All five species exhibited asymptotic functional space accumulation curves, suggesting a limit to intraspecific functional crop diversity, likely due to: dominant phenotypes represented by several genotypes; or functional redundancy that might exist among genotypes. Our findings indicate that there is a diminishing return of functional diversity with increasing number of genotypes. Our analysis demonstrates the efficacy of functional space accumulation curves in quantifying trait space occupancy of crops, with implications for managing crop diversity in agroecosystems, and genetic diversity in crop breeding programs.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".