BEEF CATTLE NUTRITION SYMPOSIUM: Feeding Holstein steers1
Bibliographic record
Abstract
One of the biggest challenges facing the cattle industry in North America (the United States, Mexico, and Canada) is cattle supply. Perhaps one of the most notable shifts in the beef industry has been the increased feeding of Holstein steers to increase cattle supply. Therefore, the 2015 Beef Cattle Nutrition Symposium of the 2015 Joint Annual Meeting of the American Society of Animal Science and the American Dairy Science Association in Orlando, FL, titled “Feeding Holstein Steers” was organized to provide an update in research developments and industry trends related to managing and feeding Holstein steers. The symposium featured 6 speakers, including Luis Burciaga-Robles (Feedlot Health Management Services, Okotoks, Alberta, Canada), Glenn Duff (New Mexico State, Las Cruces), Michael Ballou (Texas Tech University, Lubbock), Richard Zinn (University of California, Davis), T. G. Nagaraja (Kansas State University, Manhattan), and Trent McEvers (West Texas A&M University, Canyon). Speakers addressed 1 of 2 feed yard management segments: the growing period or the finishing period. These 2 periods of growth were separated given that there is a growing body of evidence that the management of calves has a large impact on how they finish.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".