Effect of Density, Cover, Depth, and Storage Time on Dry Matter Loss of Corn Silage
Bibliographic record
Abstract
Whole-plant corn was harvested at 37% dry matter (DM), finely chopped (10 mmgeometric mean length) and ensiled in 54 mini-silos of 100 mm diameter by 600 mm height. Siloswere filled at three controlled densities (160, 240, and 320 kg DM/m), either covered with a nearlyperfect seal or left uncovered, opened after 1, 2, and 6 months and replicated three times. Withineach silo, two 100-mm diameter nylon screens were placed at 200 and 400 mm from the bottomwhile filling to analyze DM loss for three separate 200 mm vertical segments. The well-sealed silosdid not exhibit any difference in DM loss as a function of density, depth or time (overall average of0.9% DM loss). The uncovered silos exhibited very sharp differences (overall average of 17.0% DMloss); DM loss was 25.9, 15.9, and 9.1% for densities of 160, 240, and 320 kg DM/m, respectively.In the uncovered silos, DM loss was 8.9, 15.5, and 26.4% after 1, 2, and 6 months, respectively. DMloss was 36.1, 12.8, and 2.0% in the first segment (0-200 mm from surface), the second segment(200 to 400 mm), and the third segment (400 to 600 mm), respectively. These results imply thatintense compaction of bunker silos (e.g. increasing density from 160 to 320 kg DM/m) will reduceDM loss especially in uncovered silos and in the top 0.4 m layer. A well sealed bunker silo isexpected to have minimal DM loss, independently of density, at a rate of about 0.5% per month.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".