Utility of Dried Distillers Grain as a Fertilizer Source for Corn
Bibliographic record
Abstract
Increased ethanol production may result in excessive dried distillers grains (DDGs) that could be utilized as a fertilizersource for corn (Zea mays L.). Research was conducted to evaluate the effects of 1) DDG rates on weed suppression,changes in soil properties, and differences in grain yield and quality and 2) DDGs, polymer-coated urea (PCU), andanhydrous ammonia (AA) fertilizer sources on grain yield and quality. DDGs had a total N-P-K composition of38.2-6.9-11.5 g kg-1, respectively. There was no corn injury, common cocklebur or jimsonweed control with DDG ratesup to 3600 kg ha-1. Corn grain yield increased 1.41 and 1.56 kg ha-1 for every kg ha-1 of DDGs applied in medium andhigh yield environments, respectively. Grain yield was ranked non-treated control < DDGs < AA = PCU whenfertilizers were applied at N equivalent rate of 140 kg ha-1 in medium and high yield environments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".