Evaluation of the Addition of Urea or Calcium Oxide (CaO) on the Recovery of Dry Matter of the By-Product of Sweet Corn Silage
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of the addition of different percentages of urea or calcium oxide (CaO) on the fermentative characteristics and chemical composition of the by-product of sweet corn silage, without whole kernel corn. The experimental design was completely randomized in a 2 × 5 factorial scheme, with two additives (CaO or urea) and five inclusion levels (0, 0.5, 1.0, 1.5, and 2.0% in natural matter of by-product of sweet corn silage). There was interaction between the type of additives and addition levels (CaO or urea) for pH values (P < 0.05). The mean pH values ranged from 3.40 to 5.36 in the additive silage. For effluent production, the additive type interaction and addition levels were significant (P < 0.05). The addition of CaO independent of the level used was not effective in reducing dry matter (DM) losses during ensilage. The total losses of DM presented a significant increase with the levels of addition of CaO, varying from 91 to 177% in relation to the control silage. The addition of urea to all levels had a satisfactory effect on the total loss of DM, ranging from 38 to 69% improvement in the reduction in relation to the control silage. The additive CaO was not efficient in reducing the fermentation losses and preserving the silage. However, urea was efficient in the recovery of DM in the ensilage process.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".