Floatation, a Bulk-Selection Method for Breeders which Permits the Selection of Faster Modifying Barley Grains
Bibliographic record
Abstract
We have confirmed that floatation in salt solutions can be used to separate less-dense, more mealy, lower nitrogen-containing barley from more dense barley with less good malting quality having more steely grains and higher nitrogen contents. This gives breeders a useful tool for mass-selecting barley with potentially favourable characteristics for malting. We have now shown that green malt can be fractionated by floatation on sucrose solutions into fractions the least dense of which contains grains which are the most completely modified. This allows grains that have malted well to be selected. Using this novel technique it has been possible to substantially enrich fractions with green malt corns of the better quality malting variety when mixtures of a good and a less-good malting barley had been micromalted. The green malt fractions can be grown on to maturity. This technique gives breeders a powerful mass-selection tool for enriching their genetically mixed breeding lines with strains of better malting quality. This approach should be particularly powerful if applied several times in successive generations. The best results are likely to be obtained by fractionating the barley, collecting the lightest material, micromalting it and then collecting the lightest fraction of the green malt for further propagation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".