Collaborative Development of a Farm Systems Learning Platform “4D Virtual Farm”
Bibliographic record
Abstract
Over the past 20 years, a lower percentage of veterinary and animal science students entering Australian and New Zealand schools have a background or ongoing contact with livestock production systems. The increasing use of digital technologies over the same time provides a practical option to introduce students to the seasonal operations on livestock farms. This article describes the development of the 4D Virtual Farm, established to showcase 11 representative livestock farms across Australasia allowing students to virtually travel through seasons and place over each farming enterprise. Students can virtually visit different beef cattle, prime lamb, wool-sheep and dairy cattle farms, and a piggery. Any electronic device connected to the web including mobile phones, tablets, computers, and virtual reality headsets can be used to view the enterprises. For educators, the virtual farm can be used for a range of teaching and learning scenarios, such as demonstration of a particular production system via weblink for lectures or embedding within learning management systems. It also allows students to start at a particular point in time and space and guide themselves to other areas for self-learning or for a range of assessment tasks. This site provides an example that could be used in other teaching areas including abattoirs, exotic diseases, surgery, communication, and many other veterinary examples.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".