HYDRODYNAMIC SEPARATION OF GRAIN AND STOVER COMPONENTS IN CORN SILAGE
Bibliographic record
Abstract
The objective of this work was to evaluate the potential of hydrodynamic separation with water tosort corn grain from stover after ensiling. In a first experiment, the specific gravity of dried intact grain wasfound to be significantly higher (1305 kg DM/m) than that of dried chopped stalk and leaf (average 635 kgDM/m) or dried chopped husk and cob (average 826 kg DM/m). However, when all material was ground,there was no significant difference between the five components (average 1546 kg DM/m). In a secondexperiment, mixing fresh silage in water resulted in partial segregation of grain from stover, achieving a grainconcentration as high as 75% in the sunk material when silage had a relatively low moisture content (64%MC) but as low as 41% when silage was relatively wet (74% MC). In a third experiment, partial drying toremove 20 percentage units of moisture prior to water separation increased grain concentration to 92% whilecomplete drying increased grain concentration to more than 99%. Sieving increased grain concentration to79%. In an industrial setting, hydrodynamic separation of silage with minimal pre-treatment could provide afeedstock with a high concentration of grain (75 to 80%). In a laboratory setting, hydrodynamic separationwith prior oven drying could provide a method to separate grain from stover in corn silage by reaching a grainconcentration higher than 99%.
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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 | 0.001 | 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".