A typological characterization of Canadian beef cattle farms based on a producer survey
Bibliographic record
Abstract
The diverse nature of beef production was captured by establishing a farm typology based on an extensive survey of 1005 Canadian farms in 2011. The survey provided information on the type of operation, cattle numbers, feed storage and management, manure management, land use, producer demographics and attitudes to risk, and technology adoption. Principal component analysis and cluster analysis were used to understand the relationships among variables and to statistically identify farm types. A total of 41 diagnostic variables from 133 survey questions were used to define 16 principal components explaining 68% of the variation. Cluster analysis yielded eight major clusters as distinct farm types. The largest number of farms (37%) was grouped as small-scale, part-time cow–calf operations. Mixed operations (crop–beef) were next most frequent (22%), followed by large cow–calf backgrounding (18%) and diversified cow–calf operations that included crop–beef mixed operations as well as off-farm activities (11%). Cow–calf operations that finished calves comprised 8% of the total farms surveyed. Extensive cow–calf backgrounding operations, large backgrounding/finishing operations, and large finishing operations represented the remaining 3% of the farms. The typology not only provides a strategy by which the Canadian beef cattle industry can be characterized, but also improves understanding of the diversity of farm management practices to help develop policies and beneficial management practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".