185 Evaluation of the Chemical Composition of Intermediate Wheatgrass (Thinopyrum Intermedium) Regrowth for Potential Fall Grazing of Beef Cattle
Bibliographic record
Abstract
Abstract This study evaluated the chemical composition of 3 intermediate wheatgrass (IWG) treatments compared with a grass/legume control for potential Fall grazing of beef cattle. Treatments were: 1) IWG, pure stand, no fertilizer post establishment (NFERT), 2) IWG, pure stand+50 kg N ha-1 annually post grain harvest (FERT), 3) IWG with Alsike clover (50:50; ACL), 4) Tall fescue/Algonquin alfalfa/Oxley II cicer milkvetch (50:25:25; FORCON). Small plots (4 replicates/treatment) were established in 2019 at 2 sites in Manitoba (Carman and Brandon). The IWG treatments were harvested in August for grain production and the regrowth sampled in October. The FORCON treatment was harvested annually in June and the regrowth stockpiled until October sampling. Forage samples were analyzed for crude protein (CP) and total digestible nutrients (TDN). Data were analyzed using PROC GLIMMIX in SAS, with the fixed effects of treatment and location, with replicate within location nested as a random effect. For CP, in 2020 at Carman, FORCON was less (10.0%) than the IWG treatments, which, in turn, did not differ (mean 19.5%). At Brandon, the opposite response was observed with FORCON greater (11.5%) than the IWG treatments (mean 8.7%). In 2021 at Carman, FERT had greater CP (14.3%) than all other treatments, which, in turn, did not differ (mean 9.9%). At Brandon, FORCON was greatest in CP (15.8%), with ACL and NFERT least (mean 8.9%) and FERT intermediate. In terms of TDN, in 2020 at both sites, FORCON was less (mean 55.3%) than all IWG treatments, which, in turn, did not differ (mean 64.4%). In 2021 at Carman, FERT and FORCON were greater (mean 67.0%) than NFERT and ACL (mean 59.5%). In Brandon, FERT was greater than ACL and NFERT, with FORCON at an intermediate value. By the second production year, the fertility treatment significantly increased the nutritive value of the IWG treatments.
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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) | 0.001 | 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".