Phenological variation in native mixedgrass chemical composition, in vitro fermentation, and methane production
Bibliographic record
Abstract
This study evaluated the impact of phenological development on chemical composition and in vitro ruminal fermentation of three cool-season ( Pascopyrum smithii (Rydb.) A. Love, Hesperostipa comata (Trin. & Rupr.) Backworth, and Koeleria macrantha (Ledeb.)), and three warm-season ( Bouteloua gracilis (Kunth) Lag. Ex Griffiths, Calamovilfa longifolia (Hook.) Scribn, and Distichlis stricta (L.) E. Greene) native grasses in the Mixedgrass Prairie of Western Canada. Grasses were harvested during June, July, and August of 2022 and 2023. Linear mixed models and simple regression models were used to analyze the data. Significant interactions between species and harvest month were observed for dry matter (DM), crude protein (CP), neutral detergent fiber, acid detergent fiber, and total digestible nutrients ( P < 0.05). CP decreased over time, except in B. gracilis, which peaked in July. Variation in fiber components and DM disappearance were species- and time-dependent. Koeleria macrantha yielded higher in vitro ruminal volatile fatty acid concentrations and acetate:propionate ratios than other species, while P. smithii exhibited the greatest DM disappearance. Methane production correlated positively with forage CP across all species. Results suggest that H. comata, D. stricta, C. longifolia, and P. smithii should be grazed early-season, and that reducing D. stricta may lower methane emissions.
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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".