Selection for Forage Yield and Composition on the Wisconsin Quality Synthetic Maize Population
Bibliographic record
Abstract
ABSTRACT Wisconsin Quality Synthetic (WQS) is a maize population that has undergone four cycles of S2–topcross selection for increased silage yield and feed quality. This study evaluated forage yield and quality for each cycle of WQS per se as well as testcrosses to two Stiff Stalk‐type testers. Linear improvement was seen in whole‐plant yield, stover yield, and whole‐plant quality both in the population per se and in testcrosses. Stover quality has not improved through selection. Starch content has increased while crude protein has decreased. Milk yields on a Mg dry matter and hectare basis have increased with selection. Changes in silage dry matter yield have been greater on a percent basis than changes in silage quality, suggesting that the current selection protocol of selecting S2–topcrosses first for yield then for quality may be more efficient at improving yield than quality. Selection directly focused on stover quality may be necessary if more rapid improvement in stover composition is desired.
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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 | 0.000 | 0.000 |
| 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".