335 Beef production on novel legume-grass summer pasture mixtures in western Canada.
Bibliographic record
Abstract
A 2-yr study was conducted using a randomized complete block design to determine the effects of grazing AC Yellowhead alfalfa (ALF) and AC Mountainview sainfoin (SF) in binary mixtures with either Tom Russian wildrye or AC Success hybrid bromegrass (HBG) in late summer at Lanigan and Swift Current, SK. Forty-eight Angus steers (404 + 18 kg in yr 1; 400 + 16 kg in yr 2) at Swift Current SK and 64 Angus heifers (364 + 51 kg in yr 1) and 48 Angus steers (338 + 23 kg in yr 2) at Lanigan SK were randomly allocated to 1 of 4 replicated (n=4) pasture mixtures, (i) ALF-RWR; (ii) ALF-HBG; (iii) SF-RWR; and (iv) SF-HBG. Data were analyzed using Proc Mixed model of SAS. Average daily gain (ADG) of steers at Swift Current were similar (P = 0.19) for all pasture mixtures. At Lanigan, heifer ADG were similar (P = 0.09) for all pasture mixtures in yr 1 ranging 0.47 to 0.66 kg d-1. In yr 2, Lanigan steers grazing SF-RWR pasture had higher (P = 0.02) ADG (1.1 kg d-1) compared to ALF-HBG steers (0.64 kg d-1). Animal grazing days (AGD) (P = 0.26) and total beef production (TBP) (P = 0.59) at Lanigan were similar in both years, for all pasture mixtures ranging from 78 to 116 AU ha-1 AGD and 58 to 78 kg ha-1 TBP, respectively. However, at Swift Current in yr 2, AGD and TBP did differ (P = 0.01) with steers grazing ALF-HBG mixtures having greater AGD (121 vs. 74 AU ha-1) and TBP (120 vs. 67 kg ha-1) compared to steers grazing SF-RWR (74 AU ha-1) pasture. These results suggest that novel legume-grass pasture mixtures are suitable for late summer grazing to improve grazing beef cattle performance.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".