Demographics of Australian horses: results from an internet‐based survey
Bibliographic record
Abstract
OBJECTIVE: To obtain information on the types of Australian horses, how they are kept and their activities. METHODS: An invitation to participate in an opt-in, internet-based survey was sent to 7000 people who had registered an email address to receive information from the Australian Horse Industry Council Inc. RESULTS: There were 3377 (48%) useable responses from owners of 26,548 horses. Most horses were kept on small properties (usually 2-8 ha) in paddocks in rural areas of Queensland, New South Wales and Victoria. Most horses were female or geldings and the most common of 54 different activities was breeding. Owners reported 19,291 horses were used in different activities and 6037 (23%) horses were not kept for any stated purpose or activity. Owners used an average of 1.95 horses in 2.9 different types of activities. The most common of the 43 breeds were Thoroughbred, Australian Stock Horse and Australian Quarter Horse. Only 1% of the total numbers of Thoroughbreds and Standardbreds in this survey were used in horse racing, indicating there is a demand for these breeds in non-racing activities. Microchip was the most favoured method of horse identification and 36% favoured compulsory registration of horses. Most respondents reported owning some other animal species. CONCLUSIONS: There is a wide variation in horse breeds used in different activities by Australian horse owners. There are regional differences in various management systems. There needs to be considerable improvement in the collection and recording of information to improve the validity and reliability of horse industry data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".