Leveraging Data from the Genomes to Fields Initiative toInvestigate G×E in Maize in North America
Bibliographic record
Abstract
Abstract Genotype-by-environment (GxE) interactions play a significant role in crop performance and stability. Investigating GxE requires extensive data where diverse genotypes are tested over multiple locations and years. Since 2014, the Genomes to Fields (G2F) initiative has collected phenotype and genotype data for more than 4,000 diverse hybrids tested in more than 130 year-locations combinations in the US and Canada. We curated this data set and expanded it by generating (using a crop model) environmental covariates for each of the trials conducted by the G2F initiative since 2014. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits in North America. We used multivariate analyses to characterize the data set’s genetic and environmental structure, study the association of key environmental factors with grain yield and flowering traits, and provide benchmarks using state-of-the-art genomic prediction models. The workflows used to generate and analyze the data set are provided as open-source code. The data set that we introduce in this study can serve as a benchmark in agricultural modeling and prediction, and paves the way for countless GxE investigations in maize.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".