In vitro degradation and methane production of short-season corn hybrids harvested before or after a light frost
Bibliographic record
Abstract
In western Canada, short-season corn silage production is increasing due to its potentially high nutritive value. The objective of this study was to determine variability and relationships among nutrient concentration, degradability, and methane (CH4) production of short-season whole-plant corn hybrids harvested before or after light frost (−1.5 °C). Four hybrids, based on their corn heat unit rating (≤2600, CHU rating), were grown in 2 yr in central and southern Alberta (AB) with two field replications. The batch culture and Daisy fermenter techniques were used to characterize degradability and gas production measurements. At both locations, dry matter (DM) concentration was affected by harvest and hybrid (P ≤ 0.02). However, starch and neutral detergent fiber (NDF) concentrations differed (P ≤ 0.01) or tended (P = 0.07) to differ among harvest and hybrid only in central AB. Over both locations and harvest times, CH4 production was related negatively to propionate and positively to acetate proportions. In conclusion, harvesting southern AB hybrids after frost increased DM concentration and NDF degradability with no effect on CH4 emissions, but the high DM concentration may negatively affect silage quality and animal performance. Harvesting central AB hybrids after frost increased DM and starch concentrations, while reducing CH4 emissions but had limited effects on nutrient degradability.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".