415 Evaluating the yield and nutritive value of 7
Bibliographic record
Abstract
This study assessed the relative potential of 7 annual species for stockpiled forage production to extend the grazing season for beef cows. The species and cultivars were: corn (Zea mays L.) cv. Fusion; foxtail millet (Setaria italica (L.) P. Beauvois) type Golden German; oat (Avena sativa L.) cv. Haymaker; fall rye (Secale cereale L.) cv. Hazlet; barley (Hordeum vulgare L.) cv. Maverick; annual ryegrass (Lolium L.) cv. Aubade; and soybean (Glycine max (L.) Merr.) cv. Mammoth. Plots were seeded at Saskatoon, Saskatchewan in 2014 and 2015 in a randomised complete block design with 4 replicates per year. Stockpiled DM (SDM) yield was determined on October 15 in 2014 and 2015. There was no difference in mean SDM between years with 6.2 Mg ha-1 and 6.4 Mg ha-1 for 2014 and 2015. Oats and millet consistently had high SDM across years, with fall rye lowest (2.6 Mg-1 ha). The yield of corn, however, was 2 times higher in 2015 (15.8 Mg ha-1) than in 2014 (6.8 Mg ha-1). Species CP differed significantly (P<0.001), with fall rye and soybean highest. The CP concentrations of other species did not differ ranging from 60.2 g kg-1 (millet) to 82.3 g kg-1(ryegrass). Forage TDN exhibited significant a species by year interaction (P<0.001). Mean TDN concentration in 2015 was higher than that in 2014 (P<0.001). Ryegrass, barley and oats ranked higher in TDN in 2015 than 2014. All the other species had TDN values that did not differ between years. This study demonstrated that annual species can be used for late fall/early winter stockpiling for grazing beef cows. The TDN of all species was adequate for dry cows, with corn and fall rye having the highest SDM/energy and CP, respectively though fall rye was much lower yielding.
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 | 0.000 | 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".