Bibliographic record
Abstract
The rapid growth of corn-based dry grind ethanol plants over the past decade in the US has resulted in a great increase in production of the coproduct DDGS (distillers dried grains with solubles). Since some physical properties like low bulk density and poor flowability can impact the market potential of DDGS, pelleting of DDGS can be one of the easiest ways to improve this situation. Pellet quality is the focus of this project. The pelleting process was conducted with three initial DDGS moisture contents and two different dies; a total of six runs were completed to produce DDGS pellets. The physical qualities of pelleted DDGS were determined by measuring durability, bulk density, angle of repose, and color of the pellets. The results showed that the durability ranged from 42% to 89%, the highest pellet durability occurred when the moisture content was 20% db and the die diameter was 1/8 in. The bulk density increased when the DDGS moisture content decreased, and the highest bulk density was observed when the moisture content was 10% db and the die diameter was 1/8 in.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".