Virtual Feedlot Shortcourse: When Life Hands Out Lemons
Bibliographic record
Abstract
Objective The COVID-19 pandemic forced changes in how Extension programming was delivered in 2020. Web-based distance learning tools were used to deliver educational material when it was impractical to use traditional delivery methods. Study Description The SDSU Extension Feedlot Shortcourse has traditionally been an in-person event with as much opportunity for hands-on learning and demonstrations as possible. The program is offered over a two-day period in August at the SDSU Cow-Calf Education and Research Facility with approximately 30 participants each year, on average. The program addresses feed delivery and mixing, animal health, production technologies, and risk management. However, the events of 2020 turned that plan on its head. It was clear by early summer that holding in-person events would be challenging at best, with the very real risk of being forced to cancel or postpone because of changing conditions surrounding COVID-19. For that reason, we elected to offer the Feedlot Shortcourse as a virtual program using the Zoom platform. The first challenge was to attempt to replicate the program without being face-to-face. We selected seven topics that were relevant to successful backgrounding or cattle finishing enterprises that could be taught effectively on a virtual platform. Those topics and presenters were as follows in alphabetical order by topic: Backgrounding Systems – Dr. Alfredo DiCostanzo, University of Minnesota Beef Specialist Bunk Management – Warren Rusche, SDSU Extension Beef Feedlot Management Associate Cattle Feeding Risk Management – Dr. Matt Dierson, SDSU Extension Risk Management Specialist Facility Management – Dr. Erik Loe, Midwest PMS Feedlot Cattle Health Strategies – Dr. Russ Daly, SDSU Extension Veterinarian Growth Enhancing Technologies – Dr. Zach Smith, SDSU Feedlot Researcher Wrap-up Panel Discussion The webinar series was held on seven consecutive Thursdays in July and August at 12:30 CDT for approximately one hour. Each session was recorded so that participants could watch at their convenience if they were unable to log on for the live sessions or wished to view the program again. Participation in the program greatly exceeded expectations. There were 275 registered participants from 25 states plus Canada, Mexico, Brazil, Australia, and South Africa.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.016 |
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".