Fermentation profiles and degradability measurements in extrusa diet samples collected from brome-suppressed and undisturbed pastures and their relationship to weight gain of steers
Bibliographic record
Abstract
The effects of suppression of annual bromes (Bromus japonicus Thunb. and Bromus tectorum L.) by atrazine application on the nutritive quality of extrusa diet samples (EDS) collected from the esophagus were investigated, and EDS quality estimates were compared with weight gain of grazing steers. Analysis on EDS included crude protein (CP), in vitro organic matter degradability (IVOMD), and gas production profiles in N supplemented and unsupplemented incubation media. Brome-suppression tended (P = 0.07) to increase CP content but effects on gas production kinetics and IVOMD were dependent on incubation medium N-level. In N-unsupplemented incubations, asymptotic gas production was less and rates of gas production were greater in EDS from brome-suppressed compared to undisturbed pasture. No such differences were found for N-supplemented incubations. Weight gains of steers grazing brome-suppressed pastures were 16% greater (P = 0.007) than from control pastures. The R2 for the comparison of predicted and measured gains were 0.90 (P < 0.0001), 0.96 (P < 0.0001), and 0.90 (P < 0.0001) using CP, IVOMD (N-low), and IVOMD (N-rich) as the predicting variable, respectively. Best predictions using in vitro gas production measurements were obtained from 24 h gas volume recording (R2 = 0.93, P < 0.0001). Best-fit model (sigmoidal vs. exponential) depended on grazing period and N-level, and the sigmoidal Gompertz model best described most gas production profiles. Key words: Forage quality, gas production, weight gain, beef steers
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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".