Empirical comparison of genomic selection to phenotypic selection for biomass yield of switchgrass
Bibliographic record
Abstract
Abstract Switchgrass (Panicum virgatum L.) is one of several grass species being bred for use as a biomass crop to support the biofuel industry. Increases in biomass yield are imperative to ensure that crops such as switchgrass can sustainably meet the needs of this industry. Genomic selection is one strategy that can accelerate breeding gains for complex traits such as biomass yield. The goal of this study was to conduct three cycles of genomic selection in a previously trained Liberty switchgrass population and compare that to one cycle of phenotypic selection, both of which required 3 years to complete. The advanced lines were tested across five locations and three hardiness zones in the Central United States using a randomized complete block design with four replicates. There were strong genotype × location interactions, but the first two generations of genomic selection were superior to Liberty at four of the five evaluation locations. Conversely, phenotypic selection failed to result in significant gains in biomass yield for any of the five evaluation locations. Based on these results from Liberty switchgrass, genomic selection methods are expected to at least double the rates of gain in biomass yield relative to previous estimates using phenotypic selection methods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".