Predicting the performance of beef cattle on diverse perennial forage mixtures using the cowbytes ration-balancing software program
Bibliographic record
Abstract
The objective of this paper was to evaluate 29 perennial forage treatments consisting of five monoculture grasses as control, six simple mixtures, and 18 complex mixtures using the CowBytes® ration-balancing software and predict the performances of steers, dry gestating, and lactating beef cows over 3 years of forage harvest (2021, 2022, and 2023). Barley straw, a cheaper feed source was included in the model as supplement to standardize final ration quality when forage by treatment exceeded requirements. We found that the inclusion of straw reduced the daily ration cost. Predicted steer average daily gains was higher (0.80 kg day −1 ) on complex grass–legume mixtures in 2021 and 2023, whereas in 2022, simple mixture of Fleet meadow bromegrass (MB) + AC Yellowhead alfalfa (M1), and timothy grass (TG) monoculture treatments predicted higher gains. For dry gestating beef cows, a mixture of Fleet MB + AC Yellowhead alfalfa + AC Mountainview sainfoin + Veldt cicer milkvetch (M9), and orchardgrass (OG) monoculture predicted a gain of 0.10 kg day −1 in 2021, while in 2022 and 2023, simple mixture of Fleet MB + Spredor 5 alfalfa (M4), and complex grass–legume mixtures predicted a gain of 0.10 kg day −1 after supplementing with straws. For lactating beef cows, in 2021, complex grass–legume mixtures estimated a gain of 0.10 kg day −1 . In 2022 and 2023, monocultures of OG, Kirk crested wheatgrass, and TG met the predicted gain requirement of 0.10 kg day −1 . Over the 3 years, steers, gestating, and lactating cows performed well when fed with legume-dominated mixtures in the first year of forage production, but monoculture grasses and grass-only mixtures suffice nourishing them in the second and third years. Overall, our study highlighted differences in predicted animal performance across yearly harvested forages.
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) | 0.000 | 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) | 0.001 | 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".